Average Fatty Acids and B2 index of different animals and human beings.
\r\n\tgas sensors.
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D in Physics in 2012 from Indian Institute of Technology Guwahati, India. Presently, he is associated with the Faculty of Science, Sri Sri University, India as an Assistant Professor in Physics. Prior to joining the current\naffiliation, he was a postdoctoral fellow at different renowned institutions, Kobe University Japan, S. N. Bose National Centre for Basic Sciences, India and Cardiff University, United Kingdom. He was awarded prestigious JSPS postdoctoral fellowship based on his research contribution on semiconducting nanowires. He has published more than 32 research articles including 1 review article in high profile international journals and 3 book chapters to his credit. 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His research interests include the design and characterization of portable\r\nbiosensors, biodevices and sensor interfaces for miniaturized systems and biomedical applications for point-of-care testing. He received his Ph.D in Biosensor and Electrochemistry from Pusan National University, South Korea,\r\nwhere he developed different class of electrochemical sensors and studied the electrochemical properties of gold, platinum, and palladium based metal electrodes. He completed his Post-doctoral fellowships in the Department of\r\nMechanical Engineering, Michigan State University, USA and Department of Electronic and Electrical Engineering at University of Bath, UK. 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The document, which is accomplished today, appears firmly rooted in traditional conservative psychiatry ignoring the progress made by the biological research field. Clearly, the dichotomy between conservative and progressive psychiatry is not over, despite the efforts of the scientific research in the field of psychiatry, of brain, of neurotransmitters and of quantum computation of the brain and consciousness, i.e., the disciplines that belong to neuroscience. It seems correct, from the point of view of ethics, remember how it is difficult to think of the research in psychiatry as completely independent of influential external factors (kuhnian paradigms).
Recently some major events have allowed a movement of thought not only innovative, but of profound and insistent criticism, mainly at a high intellectual and scientific level, on the ideological implications of psychiatric diagnosis and of the increasing complexity of the nuances that classify the psychiatric disorder, rather than looking at a window that allows, through biological markers, a reliable diagnosis and appropriate care in the first diagnostic instance by limiting the diagnostic error unaware that psychiatric diagnosis has dragged on for years about the recognition of bipolar disorder from major depressive disorder [1] where there is a diagnostic misinterpretation ranging from 40% [1] to 70% (Tenth World Day for the Prevention of Suicide, Rome, 2012).
The fifth edition has been criticized by a number of authorities, even before it was formally published. The main thrust of criticism has been that changes in the DSM have not kept pace with advances in scientific understanding of psychiatric dysfunction. Another criticism is that the development of DSM-5 was unduly influenced by input from the psychiatric drug industry. A number of scientists have objected that the DSM forces clinicians to make distinctions that are not supported by solid evidence, distinctions that have major treatment implications, including drug prescriptions and the availability of health insurance coverage.
In the first experimental phase two mathematical tools were identified, one complex (the Self-Organizing Map-SOM) and a simple one (the Index B2) which in time will prove valuable not only to define the condition of the Major Depression and Bipolar Disorder, but will provide the possibility of reasonable inferences about the biological significance of the two molecular mood disorders.
The SOM is an artificial neural network that has the ability to put together similar objects and distant different objects using the characteristics of the objects considered.
The index B2 (so named by the authors of the research, (namely, Cocchi and Tonello) is derived from a mathematical operation that relates the characteristics of molecular weight and melting point of the fatty acids isolated and recognized, by the SOM, to have the power of recognizing the two disorders.
Proceeding by grades we will say that the two groups of subjects investigated in the first phase of the research (apparently normal and depressed) were determined by the fatty acids of platelets, having chosen this cell type for the morphological and functional particularities that distinguish these cells, i.e., the presence of receptors for neurotransmitters, particularly serotonin, and because they are also the seat of the molecular events that regulate the hemo-coagulation process.
The results obtained experimentally, interpreted by the non-linear mathematical function, the SOM, and the index B2 showed the ability to distinguish "psychiatric" patients from "normal" ones. The problem was that the psychiatric diagnosis we had received was generally expressed as Major Depression, while the arrangement of subjects within the framework resulting from the SOM and the index B2 induced us to think that some aspect was unclear about the diagnosis that we had received.
Placement in the SOM and the evaluation indexes B2 (negative and positive) led us to look for an opportunity to relaunch the experiment that, as we thought, might be able to recognize the apparently normal individuals from major depressive and bipolar.
The opportunity came with a grant from the Marche Region, and in two years of intense work, including the contributions of psychiatrists, biochemists, molecular biologists, mathematicians and quantum physicists. A research that has used a combination of biology and nonlinear mathematics was carried out, in order to identify, within the psychiatric chapter of mood disorders, whether it was possible to identify in the platelets, and in particular in their fatty acids, molecular features that could allow a clear and precise classification of subjects with Major Depression (MD) and with Bipolar Disorder (BD). The results were obtained using an Artificial Neural Network, in particular a network called Self-Organizing Map (SOM) [2, 3]. The SOM is an unsupervised competitive-learning network algorithm, which was created by Teuvo Kohonen in 1981–82 [4-6].
With the above combination, using platelet’s Palmitic Acid, Linoleic Acid, and Arachidonic Acid together with SOM and a mathematic index (B2), it was possible to obtain the effect of discriminating between MD and BD, for the first time in years. The B2 index was obtained from the summation of the percentages of each fatty acid multiplied by its melting point and divided by its molecular weight, obtaining an indirect expression of membrane viscosity, which induces us to identify it with the neuron membrane viscosity [7]. The B2 index is found to be negative in MD and positive in BD, that is the membranes in MD are, by far, less viscous than in normals, in BD, in psychotics, showing a unique and specific molecular characteristic for subjects with MD [8]. On these bases it was possible to explain the quantitative biomolecular approach to major depression and hypothesize that in mood disorders a biomolecular pathway exists, moving from cell membrane viscosity through Gsα protein and Tubulin [9]. We got the result so desired and hoped to confirm that the guess was correct, the depressed subjects were distinguished from bipolar, beyond all psychiatric, classificatory and interpretative dialectics. Figures 1, 2, 3, 4, 5 summarize the main steps of the research. In front of the results, we began to reflect on the distribution and on the logic of the numbers that had been so intimately associated with psychiatric conditions, as they represented a fact which did not take into account therapies and nothing that from the outside could be related to subject. All this led us to think that there was something already written in platelets that can simulate the condition of the neuron, at least, as regards the levels of serotonin. On this observation we wrote some articles that related to the uniqueness of the molecular Major Depression, the role of membrane viscosity and molecular reflections on the state of consciousness and the mechanical strength of the membrane.
During the last experiment [10] the psychiatrists have provided us with eight cases of “Suicidal Ideation”.
When we have classified them over the SOM, the Figure 6 was obtained.
Distribution of all cases [apparently Normal (white) and Pathologic (red) over the SOM obtained by Platelets’ Palmitic Acid (PA), Linoleic Acid (LA) and Arachidonic Acid (AA).
shows the pathologic subjects Major Depression and Bipolar Disorder) all together.
(a new SOM has been realized) shows, clearly, that it was possible to distinguish the subjects with Major Depression (red) from those one with Bipolar Disorder (blue).
shows the same picture of
In
The cases were collected where the SOM recognizes the minimum of Linoleic Acid. In particular seven cases were Bipolar and one with Major Depression, confirming that both can have suicidal ideation and can attempt suicide [10]. The subject, in position 15:4, was uncertain at the psychiatric evaluation; in effect his position is a little bit out from the critical area of the minimum of Linoleic Acid. In the same way, other areas has been found within the SOM (fig. 7): Obsessive Compulsive Disorder (OCD) area, Major Depression area, Bipolar area (the largest), Psychotic area etc.
Distribution of the “suicidal” cases over the SOM.
All the experimental findings in humans and animals are resumed in Figures 7 and 8.
Distribution over the SOM of human subjects and animals. According to the psychiatric diagnosis (when definitive) we can recognize: 1= OCD area, 2= Major Depression area, 3= Bipolar area (the largest), 4= Suicide area, 5= Psychotic area, N= apparently normal area. N area collects about the 50% of the sample of subjects considered apparently normal.
Distribution over the SOM of human subjects and animals. According to the psychiatric diagnosis (when definitive) we can recognize: 1= OCD area, 2= Major Depression area, 3= Bipolar area (the largest), 4= Suicide area, 5= Psychotic area, N= apparently normal area. N area collects about the 50% of the sample of subjects considered apparently normal.
Several different animals have been mapped on the SOM, as well. The molecular similarities [11], observed between animal and man (Figure 8), concern the conditions of Major Depression, Bipolar Disorder, Obsessive Compulsive Disorder (OCD).
To confirm the molecular correspondence between man and animal, observe how, e.g. Cat, Bovine, Horse and Donkey, correspond to the area of maximum Linoleic Acid and of Obsessive Compulsive Disorder.
This area is recognized as the point of maximum concentration of Linoleic not only for diagnostics correspondence, but also because it contains the cat, who, as feline, is known to possess desaturase, but with low activity [12], therefore not to be able to transform Linoleic Acid into Arachidonic Acid, resulting in savings of Linoleic, and long living animals [13]. Further, in the same animals, symptoms of OCD can occur [14, 15].
Let’s suppose we want to build a fake SOM, that is, a SOM driven by us according to a desired result. We should be very lucky, in fact we should guess:
1200 particular numbers (starting weights). By the way, really, it is impossible to know how they could be chosen in order to obtain a particular result.
Above all, we should find a particular order of data that, because of an unknown reason (really unknown), lead to a very particular result. In our case, we have a data base of 144 Subjects (84 depressive and 60 normal). This means that there are 144!=5.5503*10249 combinations. A training process takes about 4 minutes. So, we need about 4.224*10242 centuries to check all possible results thus taking a particular one. A bit difficult.
So, if we want to build a fake SOM, well, it’s almost impossible (at least in a reasonable time even using the fastest computer on Earth).
The SOM (Figure 1) shows that major depressive subjects belong to an area which is completely disconnected from that of healthy and bipolar. Looking at the location of the data over the SOM, we find also a region (extreme left corner) which we attribute to psychotic subjects according to the clinical diagnosis. We translated these facts in terms of symmetry breaking [16), confirming that MD is a disease completely trapped apart from healthy, bipolar, psychotic subjects and patients with obsessive compulsive disorder (OCD) (Figure 9).
Heraclitus
Bipartition of U. U is the Universal set representing humankind. A is the cell whose elements are characterized by a positive value of B2. AC, which is the complement of A in U, is the cell whose elements are characterized by a negative value of B2. B2 is the index who put in relation the three fatty acids, isolated by the SOM, with the molecular weight and the melting point and that has recognized the Bipolar subjects (B2 positive) from the Major Depressive subjects (B2 negative).
The Quantum Paradigm Psychopathology Group (QPP) conference in Palermo (26-27 of April, 2013) has marked a definite turning point in the foundational perspective of many of the group’s participants regarding the study of psychopathology, particularly mood disorders. One reason for this turning point stems from a realization that two of the most common forms of psychopathology, major depression and bipolar disorder, may be recognizable through bimolecular markers (
The idea, as stressed during the Conference in Palermo, was to take into consideration consciousness processes, together with their normal and pathological dynamics, without fixating on isolated elements and levels that can be observed by a privileged and detached observer, but rather by thinking and acting effectively through connections, relations, and networks. And all of this, always in the belief that science is the narration of a world that expresses emerging states, and is therefore never reducible to a simple sum of basics ingredients, where spontaneous symmetry breakings ensure multiplicity, creativity, vitality, in compliance with the concept of natural self-organization and systems evolution, towards growing complex and unpredictable states.
The socio-economic significance of this procedure is undeniable: science takes shape into social and economic structures which, by accepting the transformation from foucaultian monitoring and control instruments (ideological reductionism) into open, fluid, emerging systems (complexity or open logics), could really, when considering mental diseases, understand the often blurred classifications of the DSM and open up to the important connections between consciousness and quantum brain dynamics.
Hence the rejection of any form of ontological reductionism, which is self-referring, linked to metaphysical-ideological cognitive dynamics, and tied to a pervasive will to power which sees research freedom as a worrying system breakdown.
Against this epistemological backdrop, among the foundational innovators we can mention those who have left particularly fertile footprints in terms of basic quantum theories linking brain, behaviour, and consciousness.
Quantum Mind has been an ongoing field of study since the final decades of the last century. Pioneers like the physicists Hiroomi Umezawa, Kunio Yasue, and Giuseppe Vitiello, mathematicians like Roger Penrose, and biomedical investigators like Stuart Hameroff, Gordon Globus, and Gustav Bernroider have plumbed the depths of subatomic structure and its macroscopic amplifications in search of substrates for quantum computation and other capabilities that may match attributes of the normal human psyche better than models advocated by conventional cognitive neuroscience.
In the domain of psychopathology, Gordon Globus has gone on to propound a highly original concept of schizophrenia linked to the “tuning” of quantum vibrations suffusing the brain. Nancy Woolf, along with co-authors including Jack Tuszynski, has offered credible links between psychopathology and quantum-computational dysfunction within the skeletal proteins giving shape to brain cells. Paavo Pylkkanen has related the physical substrates of mental illness to quantum “pilot waves.” Donald Mender has proposed ways of comprehending the neurophysiology of disordered thinking and emotion in terms of quantum "phase transitional" analogies to the freezing and melting of ordinary matter; he has also contributed to a reframing of psychiatric disease nosology in light of the anthropic principle. Ursula Werneke has complimented this anthropic reconsideration through her examination of psychotically “impaired” reality-testing in the context of Hugh Everett’s many-worlds ontology. Massimo Cocchi, Lucio Tonello, Fabio Gabrielli, have forged links between serotonin and quantum phenomena via membrane biophysics in depression and psychosis.
The above honor roll of seminal QPP theoreticians is surely not exhaustive, but these brief remarks are not intended as a complete historical review. Rather, the purpose is an opening into the possibility of turning today’s theoretical potentialities into experimental confirmed reality. It should be recalled and emphasized as a guiding principle that the cohesion of a convivial multidisciplinary group operating without the winnowing constraints of competing, mutually exclusive ideas may not remain true to the epistemic rigors of science. QPP can minimize this sort of hazard by maximizing, in the spirit of Karl Popper, exposure of its most cherished conjectures to a fair risk of experimental refutation.
A number of participants in the Palermo conference have signed a document, aptly called “The Declaration of Palermo,” whose conclusion asserts:
The Declaration of Palermo was written by Donald Mender and Massimo Cocchi and edited by: Don Michele Aramini, Gustav Bernroider, Francesco Cappello, Fabio Gabrielli, Gordon Globus, Mansoor Malik, Efstratios Manousakis, Kary Mullis, Eliano Pessa, Massimo Pregnolato, Paavo Pylkkänen, Mark M. Rasenick, Lucio Tonello, Jack Tuszynski, Giuseppe Vitiello, Ursula Werneke, Paola Zizzi.
This strong theoretical statement invites an opening into possible experimental models that will test the reality of the group’s hypotheses by identifying, starting from precise molecular reference points characterizing the two mood disorders mentioned above, a non-trivially quantum pathway of bio molecular changes conditioning brain processes through the most intimate aspects of neuronal, trans neuronal, and sub neuronal function. In particular, membrane viscosity and its role within the interactome may prove to figure centrally in quantum-chemical transduction of neural signals.
The Declaration of Palermo concerning the plausibility of a quantum basis for consciousness entails a lucid analysis of phenomenologies crucial to both human beings and other creatures. The main feature belonging intrinsically to both Homo sapiens and non-human animals is a common core awareness that is nevertheless expressed differently for each kind of organism at divergent levels overseeing management of disparate needs and actions, realized through behaviour in relation to concrete variations of the external environment. The dimension of “self-consciousness” is evolved, step by step, in phylogenetic progression according to an admirable order justifying the survival of each unique life form with respect to the particular tasks which it has to perform.
Today we are equipped with many high-end tools in our attempts to understand all the steps in the evolution of consciousness, but it is through intuition that we will achieve, simply, an adequate interpretation of consciousness itself, that most complex and extraordinary gift. Pending such ‘intuition,” some members of the QPP group have decided to submit to classical experimental testing those insights that each contributor has independently adduced through theoretical inquiry, that is, through the construction of an empirical map laying bare the most germane trans neuronal, neuronal, and sub neuronal molecular changes with an eye toward the possibility of inducing and measuring changes in membrane viscosity correlated with in vivo manifestation of mood disorders. As far as we know this will be the first time that such micro-molecular events are to be tied rigorously to molar cognitive phenomena.
The resulting experimental data may offer an enduring empirical anchor in contradistinction to the intersubjective vagaries that have afflicted those various psychiatric disease nosologies, most recently DSM V, issuing from the hollow consensus of committees and cultural contextual fashion. If the experimental program planned by the QPP group succeeds, the goal of psychodiagnostic validity, heretofore sacrificed by DSM to mere inter-rater “reliability”, may at last be achieved.
There is full knowledge that each mood disorder will manifest with different states of consciousness [17, 18].
Our platelets results, in their correspondence with the strong diagnostic power between Bipolar Disorder and Major Depression, and in the similarity found between human and animals, give the possibility to investigate the different neuronal genetic expression, and the possible inherited errors in neurons. The working hypothesis should provide neurons, from different animal origin, which are known to have molecular characteristics similar to those of man with mood disorders. Table 1 and Figure 10 [19-24].
This could allow understanding, first, the different gene expression according to the different psychiatric disorders studied; second, culturing the neurons belonging to the different animals, it will be possible to arrange modifications of the cell membrane viscosity. In agreement with the assumption, the path described can reasonably lead to the possibility to artificially create models of membrane viscosity corresponding to changes of the psychopathological phenomenon with the ability to achieve the set of molecular evaluations necessary for the understanding of the modifications of the interactome (
The Q-NeMoMa project, practically, wants to investigate the molecular modifications of the neuron according to different modifications of the viscosity of the neuronal membrane.
Some of the most important world experts have come together to identify the experimental procedures to be carried out.
\n\t\t\t\t | \n\t\t|||||
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Sheep | \n\t\t\t4 pool of 3 | \n\t\t\t19.91 | \n\t\t\t8.22 | \n\t\t\t4.73 | \n\t\t\t3. 980 | \n\t\t
Bovine | \n\t\t\t4 pool of 3 | \n\t\t\t18.37 | \n\t\t\t26.72 | \n\t\t\t6.77 | \n\t\t\t2.937 | \n\t\t
Cat | \n\t\t\t4 pool of 3 | \n\t\t\t17.45 | \n\t\t\t27.75 | \n\t\t\t9.54 | \n\t\t\t2.240 | \n\t\t
Horse | \n\t\t\t4 pool of 3 | \n\t\t\t14.8 | \n\t\t\t23.17 | \n\t\t\t6.46 | \n\t\t\t2.173 | \n\t\t
Donkey | \n\t\t\t8 pool of 3 | \n\t\t\t14.39 | \n\t\t\t19.68 | \n\t\t\t6.34 | \n\t\t\t2.154 | \n\t\t
Guinea pig | \n\t\t\tLiterature | \n\t\t\t17.4 | \n\t\t\t12.4 | \n\t\t\t14.6 | \n\t\t\t1.675 | \n\t\t
Rat | \n\t\t\tLiterature | \n\t\t\t24.40 | \n\t\t\t9.5 | \n\t\t\t20 | \n\t\t\t2.567 | \n\t\t
Pig | \n\t\t\t80 | \n\t\t\t26.09 | \n\t\t\t8.78 | \n\t\t\t14.12 | \n\t\t\t3.957 | \n\t\t
German Shepherd | \n\t\t\t7 | \n\t\t\t18.9 | \n\t\t\t21.5 | \n\t\t\t20.39 | \n\t\t\t0.936 | \n\t\t
1 | \n\t\t\t15.13 | \n\t\t\t20.65 | \n\t\t\t22.00 | \n\t\t\t-0.240 | \n\t\t|
Alaskan Malamute | \n\t\t\t5 | \n\t\t\t18.25 | \n\t\t\t19 | \n\t\t\t21.2 | \n\t\t\t0.688 | \n\t\t
1 | \n\t\t\t16.7 | \n\t\t\t17.89 | \n\t\t\t23.93 | \n\t\t\t-0.120 | \n\t\t|
Humans (normal) | \n\t\t\t60 | \n\t\t\t20.68 | \n\t\t\t19.41 | \n\t\t\t14.06 | \n\t\t\t2.445 | \n\t\t
Depression 1 (MD+ BD) | \n\t\t\t84 | \n\t\t\t17.92 | \n\t\t\t16.71 | \n\t\t\t19.03 | \n\t\t\t1.002 | \n\t\t
Depression 2 (MD) | \n\t\t\t41 | \n\t\t\t17.22 | \n\t\t\t9.34 | \n\t\t\t26.81 | \n\t\t\t-0.310 | \n\t\t
Bipolar | \n\t\t\t67 | \n\t\t\t19.75 | \n\t\t\t8.65 | \n\t\t\t23.79 | \n\t\t\t0.819 | \n\t\t
Ischemia 1 | \n\t\t\t50 | \n\t\t\t23.32 | \n\t\t\t10.51 | \n\t\t\t15.17 | \n\t\t\t3.072 | \n\t\t
Ischemia 2 | \n\t\t\t87 | \n\t\t\t19.59 | \n\t\t\t4.74 | \n\t\t\t12.72 | \n\t\t\t2.658 | \n\t\t
Young adults | \n\t\t\t45 | \n\t\t\t18.16 | \n\t\t\t21 | \n\t\t\t14.71 | \n\t\t\t1.690 | \n\t\t
Children | \n\t\t\t59 | \n\t\t\t23.23 | \n\t\t\t11.82 | \n\t\t\t10.77 | \n\t\t\t3.746 | \n\t\t
Average Fatty Acids and B2 index of different animals and human beings.
Distribution of animals and humans over the SOM.
From this important research will be possible to obtain data needed to assess whether, corrective actions for the improvement of the devastating conditions of all those who are suffering from mood disorders, will be possible.
A valuable help to the understanding of the neuron functioning can come from quantum molecular computation, by being able to interpret the neuron modifications, in the occurrence of the most important mood disorders such as Major Depression and Bipolar Disorder.
The suggested path could start from the largest scale: the cell membrane.
Five parallel approaches should be addressed, working one with the other, Figure 11:
Quantum chemical scale of neural signals by Bernroider [25, 26].
The Fatty Acid profile (Palmitic, Linoleic and Arachidonic Acids dynamics) of Cocchi and Tonello [2, 3, 7, 8, 9, 10, 11].
The role of lipid raft and G protein of Mark Rasenick [27-29].
Cytoskeleton modifications (Microtubules and Tubulins) studied by Tuszynski [30-33].
The exosomes studied by Francesco Cappello [34].
The steps of the project
The complex dream we are running is to realize the molecular hypothesis of consciousness, designed in 2008 (private meeting in Bologna, Department of Veterinary Medical Sciences) by
The consciousness molecular path
The whole path could be supervised by Gabrielli (Philosopher [8]) and Mender (Psychiatrist [35]), scientists of rigorous intellectual skills with a profound vision of the theoretical and conceptual aspects of psychopathology and quantum consciousness.
Although there is evidence of a continuing effort by the international psychiatric community to refine the diagnosis of mood disorders, to date, the traditional diagnostic criteria are not enough sensitive in identifying patients with Bipolar Disorder (BD) from those suffering from Major Depression (MD), in the first diagnosis. Diagnosis remains mostly late and treatments, that may improve symptoms and quality of life, continue to be preceded by interventions which, in addition to not providing adequate relief, often worsen the BD course, increasing the likelihood of inducing rapid cycles or suicidal behaviour [50, 51].
Differential diagnosis of BD symptoms from other diagnoses has been documented as difficult [52-55]. Diagnosing BD from MD, psychosis, borderline personality disorders, obsessive-compulsive disorder, etc.) or neuropsychological disorders (cognitive impairment, dementia, etc.) or neuropsychological disorders, has presented challenges. Moreover, manifestations are highly variable not only from patient to patient, but also in the same subject at different stages of the clinical course and in later life.
To overcome this impasse various strategies have been identified and more sensitive and specific assessment tools have been searched for discriminating the BD condition and overcome the delay of an accurate diagnosis has been particularly difficult with MD. The BRIDGE study indicated a first way to go [56], and highlight the strength of some variables such as: mania/hypomania developing during therapy with an antidepressant or other drug, mood lability developing during antidepressant therapy, 2 or more prior mood episodes, and positive family history of mania/hypomania. A debate is essential between the advocates of traditional diagnostic and therapeutic methods and advocates of emerging methods resulting from new discoveries.
Major depressive disorder and other related and nonrelated psychiatric conditions are still characterised and defined by descriptive and non-biological criteria, but it is hoped that we can adequately characterise this and other psychiatric disorders with the addition of new quantitative approaches.
Cocchi and Tonello have studied platelet membranes of depressed subjects, enlisting profiles of FAs as a possible measure of the membrane status and to determine whether fatty acids could provide indications of diagnostic help between normal subjects and subjects affected by mood disorders. In the first experimental phase, two mathematical tools were identified, a complex one (Self Organizing Map (SOM)) and a simple one (the B2 Index), which will prove valuable not only to define the condition of the Major Depression and Bipolar Disorder, but also to provide the possibility of reasonable inferences about the biological significance of the two molecular mood disorders.
We see the emergence, in summary, of some stringent theoretical and anthropological focuses:
Firstly, the distinction between first-level ontological Depression and second-level ontological Depression.
The first kind of depression, of an existential nature, expressed by the most varied cultural traditions, is rooted in our structural contingency, which in time and in becoming recognises the mark of its own finiteness (man as an anguished and depressed “natural” animal).
Second-level ontological Depression, on the other hand, refers to MD, understood as a molecular, bio-existential niche, marked cogently by serotonin and fatty acids, with its own specific “emotive tonality” [57, 58].
The centrality of the concept of situation, that is of man as a being located “here and now”, starting from biological markers able to offer the diagnosis of MD and BD an extremely real substrate, to be faced on an empirical, therefore public base, to construct a DSM that is not pregnant with pseudo-phenomenology or ideology, but which looks at the
The continuity between biology and culture, which finds full confirmation in the phenomenon of depression, whose original (ontological) biological nature (serotonin, fatty acids) thoroughly intercepts the structural (ontological) precariousness of living that the great cultural narrations have rooted in flesh and blood (ontic) existence.
The view of man as a synthesis of
To remain in the distinction, often denied only in the intentions, between
Also biology produces sense, indeed it is the original meaning on which to graft other forms of meaning, of which philosophy is undoubtedly a strong interlocutor, but alongside other forms of knowledge (biochemistry, quantum physics, biomathematics, anthropology, sociology…), as an overall, heuristic synthesis, expressive of an autonomous, therefore “adult”, approach to psychopathology [57, 58].
Lastly, it is necessary that the scientific community and the world of the clinical profession commit themselves increasingly so that psychiatry and psychology can constitute themselves as heuristic bio-analytical-existential knowledge, where the diagnosis is not placed under the Heideggerian “yoke of the idea” [60, 61] (classificatory ideology and diagnostic imperialism), but refers to convincing biological markers. In this context, comparison with the neurosciences appears inescapable, particularly in their quantistic standpoint [62-67], with all the implications, also of an ethical nature, that this involves [68-78].
In other words, to start from biology to move towards increasingly complex systems, able to integrate biochemical expressions, living and irreducible existential experiences, social and cultural contexts. Depression therefore needs to be inscribed within a horizon of unmythicised, polyvocal meaning where the biological, physiological, clinical, existential, psycho-social and anthropological aspects are set as objective a hermeneutic framework as possible.
This is all the more so at a time like the present, when a person is often appraised only on the basis of successes achieved, of objects flaunted, of products voraciously consumed, in the instant, of his social visibility, of relentless efficacy, of perfect adaptation of the “thinkable to the possible”: all contexts where the genetic and biological psychopathologies of mood are disproportionately amplified [79-83].
If the mechanistic-reductionist cognitive approaches have been characterised by the metaphor of the “edifice”, of the solid Cartesian rock, all the forms of knowledge founded on complexity theory have been characterised by the metaphor of the “network”, of thinking in relationships, in a dynamic, fluid, open manner. In the field of mental illness, this means setting aside both the organicist paradigm and the pseudo-phenomenological, “sentimentalistic”, and therefore ideological, paradigm, in order to have an integrated view of biological objectiveness and humanistic psychotherapy.
That is to say, an expression of diverse interrelated contributions from the various disciplines (psychiatry, psychology, biochemistry, anthropology, quantum physics, mathematics, philosophy).
The observer thus becomes a builder of models, a manager of complexity, giving treatment the character of a truly empathic relationship. This is all the more so where distressing pathologies are involved, such as Major Depression and Bipolar Disorder, “caput mortuum” of psychiatry, because the absence of cogent biological markers seriously compromises every form of therapy. Hence the identification of a biological platform (fatty acids of platelets) as a starting point for a correct classification of MD with respect to BD.
The identification of three platelet fatty acids (Palmitic Acid-PA, Linoleic Acid-LA and Arachidonic Acid-AA), in addition to allowing the identification of subjects affected by Mood Disorders, brought about some hypotheses which, over the time, have been proven by robust experimental data concerning also the concept of serotonin uptake on the basis of membrane viscosity. Platelets, considered cells with high affinity to neurons, have the same embryonic origin of brain and skin (ectoderm). Over the last thirty years, numerous and influential works have reported a similarity between platelet and neuron’s serotonin concentration, mainly in MD and BD.
This evidence, together with the possibility of classifying the two main mood disorders (Major Depression, MD and Bipolar Disorder, BD), led to some considerations on the molecular uniqueness of MD, generally understood as a phenomenon affecting only human beings, and, more precisely, just part of them. The identification of the characteristics that distinguish MD subjects from BD ones occurs through the different position on the SOM of the triplet of fatty acids, above mentioned, detected for each subject, and through the B2 chemical index.
In this context, we can trace the human states between normality, BD and MD, the latter considered as a bio-molecular and existential niche. Looking into the undeniable distinction made between depressed and bipolar subjects thanks to the neural network (SOM) and the chemical index (B2) for indirect assessment of platelet membrane viscosity, we asked ourselves the question of whether the molecular characteristics of subjects with MD were completely different from those of all other living beings both humans or animals. In the light of the experimental data, humans can have either positive or negative values of the B2 index. Those humans having positive values of B2 are normal (N), bipolar (B) and psychotic (P) people. On the contrary, major depressed subjects (MD) have negative B2 values. On the basis of our hypothesis, MD would be, at this point, the real disease, among all Mood Disorders, with specific molecular features and expressions of consciousness, according to the concept of Symmetry Breaking. The use of biochemistry, non-linear mathematics, and human-animal comparison leads to some reflections that are not only really close to a cultural and biological interpretation of mood disorders, but also pave the way for diagnostic perspectives and predictive interpretation patterns of the disease known as “Mood Disorder”.
It is undeniable that the depth of the
For all societies, illness is an event to be interpreted; it is not just a biological fact but also a cultural one. Basically, illness is representation, interpretation, of a portion or of all reality by individuals in a certain social context. The medical description of the human body and the illness always refer back to culturally peculiar meanings.
In strict terms, we could say that the pain articulates its meanings in suffering, which is a restless reflection on the ineluctable, and nevertheless unexpected, occurrence of the illness.
It can be understood, then, that only medical practice that does not limit itself to the biomedical dimension, which is in any case an indisputable point of departure, but is able to meet with the suffering person in his or her intimacy, can ensure, if not salvation against the perverse myth of recovery, at least dignity of the treatment as a profound ethical and existential relationship.
Hence the opening towards intimacy as a dual construction of meaning, the planning of significances contributed to both by the patient, in trusting abandon, and by the physician, as a warm, experienced responsibility. The intimacy is inhabited by the treatment (sphere of essential meanings of living) and not by anxiety (sphere of intra-worldly commerces), precisely because the experience of illness is experience of a relational wound which destructures the biological and existential narration of the subject:
Wounded in relation to his own body, in
A relational wound with respect to everyday life, whose narrative laceration provokes at first dismay, then a steady eclipse towards an indeterminate space-time, which for this reason is anguishing and inhospitable, and requires an approach that is not simply clinical but, precisely, one of intimacy, which, as a profound expression of empathy, configures itself as discretion, the word held back, the gesture experienced, total attention for the suffering countenance in a mutual exchange of meanings.
In the case of mental illnesses, then, the social, cultural weight takes on an almost transcendent value, due to the often blurred correspondences between classification and natural object, between nosology and effective reality of the illness.
When all this is recognised, what remains, excluding the interpretations of meaning, is the structural necessity, when coping with the illness
The meaning is not only the prerogative of philosophy, which certainly remains a strong interlocutor, but also of biology and biochemistry: the corporeal meaning/significance of a pathological event.
Certainly, a medicine limited to the biological fact cannot be extensive and ostensive of the illness. At the same time, however, no one should doubt
Ultimately, we need to remind the alleged monolithic custodians of the thought of Husserl, Heidegger, Jaspers, Minkowski and Binswanger that rooting the pathology in biology does not mean expropriating the sick person of his illness and making the physician a mere functionary of the organism, an all-out pathologist who ignores biographies, experiences, corporeal dynamics and relational ontologies.
If anything, biographies, relationships, cultural expressions can be preserved in all their dignity, once their genuine biological matrix has been determined.
On the other hand, we risk only metaphysical hypostatisation and, therefore, a treatment rooted in an immobile metempirical “elsewhere”.
A fruitful heuristic synthesis between
Heidegger’s figures of omnipotence
Like psychiatry and psychology—bio-reductionist and limited to the illness—so too “antipsychiatry”, which reduces the illness only to a social construction, process of control, of exclusion/inclusion managed by bio-power [44-48], unable to recognise the productivity/creativity of schizophrenic processes [49], ends up by prejudicing the genuine dynamics of the treatment.
Hence the need for an objective biological reference able to act as a cogent platform in the treatment relationship, with reference to two distressing psychopathologies: MD and BD [1].
On why you can read in the platelet, what happens in the neuron in the case of mood disorders, we are not, up to now, able to give complete answer.
It will help in trying to understand the phenomenon, the configuration of the level curves of the various fatty acids made in the SOM (Figure 13). The values of C20: 4 (Arachidonic Acid) stored in each artificial neuron ADAM were interpolated and distributed all over the map, depending on the distance weighted of minimum squares. A graphic profile, therefore, has been made and expressed in a 2D plane (Figure 14).
The value of C20: 4 stored in each artificial neuron of ADAM
The level curves were made and expressed in a two-dimensional
Following the same procedure we have identified the maximum and minimum levels of the other fatty acids (Figure 15).
Minimum level (blue) and maximum (brown) of the fatty acids identified by the SOM
If we plot all three fatty acids expressed as index B2 we obtain Figure 16.
B2 index distribution over the SOM
The index B2, seems to be a good predictor of the macro-areas (Figure 17)
Level curves of the B2 Index over the SOM
A glimmer of light appears when we realize that only with the SOM and the B2 index, together, we can accurately identify the characteristic of the subject (SOM and B2 do not know of each other but converge on the same target). This observation becomes necessary when more than one subject, with the same B2 index have two different positions in the SOM, i.e., the same index can classify subjects in different areas. This finding is very important because it means that, in addition to the reasoning on the mobility of the membrane, there is another element of conditioning, and since everything revolves around the three fatty acids previously mentioned, must necessarily be one of them, in its concentration, that makes the difference and can affect the mood profile of the subject. Even in these cases we have accurate diagnostic findings.
For a variety of reasons that we will try to make explainable, attention is especially drawn to the linoleic acid, an essential fatty acid which is not manufactured by the human or animal organism. The level curves, which have been previously mentioned, show as the absolute minimum of linoleic acid, as shown below, corresponds to the minimum point of the B2 index (Figure 18):
B2 index and linoleic acid distribution over the SOM
The fan produced by the SOM, from right to left, shows that the B2 is in progression from-2.64 To 8.23 (Figure 19).
Distribution of the B2 index over the SOM
The apparently healthy subjects are characterized by a mean value of B2 equal to 2.80.
This value is the midpoint between the extremes-2.64 (absolute minimum given by the map) and 8.23 (absolute maximum expressed by the map).
A careful analysis of the mathematical formulation of the index B2 shows that it is governed almost entirely by the Arachidonic and Palmitic Acid. Intuitively this is deduced by expressing the average values of the two fatty acids of ADAM, using level curves, which appear qualitatively symmetrical (Figure 20).
Demonstration of the symmetry of the distribution of palmitic acid (C16: 0) and arachidonic acid (C20: 4).
Are these two fatty acids that determine the macro area of a subject? Within a macro area is the intervention of linoleic acid that modulates with precision the position of the subjects. Maybe C18: 2 is the main actor in the "fine tuning"? (Figure 21).
The distribution of linoleic acid on the map represents the core of the SOM
It is likely that in the circumstances that discriminate a healthy person from a pathological, the difference of linoleic determines, however, a modification of the biochemical factors involved and / or responsible for the biochemical and pathological determinism of the “Depression”, of course declinable also on the basis of environmental / cultural influences. It should also be noted that the effects of linoleic acid on disease are relevant because of the configurations, equally balanced, of both, Arachidonic Acid and Palmitic Acid. Practically, referring to the positions of the subjects in the map ADAM, for values beyond a certain limit of Arachidonic Acid, the subject is surely depressed, as also happens for values beyond a certain limit Palmitic Acid. When B2 is in a neighbourhood of the normal value, it becomes decisive the percentage of linoleic acid. These configurations introduce, however, the concept of hyper saturation of the platelet in the case of Palmitic and that of hyper unsaturation in the case of Arachidonic. Another discriminant, between the two mentioned for the connotation of the disease, must be identified in Linoleic Acid which, in case of excess, restates the biochemical conditions for the development of the pathology. Certainly the network works beyond these operations, not yet known and interpreted probabilistically, of connection among the values that were administered.
A key point, that of linoleic acid, which requires the opening of a new chapter of considerations.
To better understand the reasoning on the data of linoleic acid, as well as his involvement in the molecular determinism of mood disorders, we must draw attention to some scientific findings that have linked, adversely, excess of linoleic acid, even for not very high concentrations, with some biological functions [84] and molecular interactions, i.e. the microtubules disruption [85].
A series of studies of cellular nutrition [86], on the effect of different amounts of phospholipids, extracted from various organs of calf (diencephalon, retina, cerebral cortex and heart), were made on chick embryo myocardial cultures.
From numerous tests, it was observed that the heart phospholipids, differently from the others, reduced, strongly, the migration speed of the cultures.
We did not understand at that time that the cardiac phospholipids, unlike the others (midbrain, retina and cerebral cortex), are very rich in linoleic acid, and that their addition, in addition to the amount naturally present, could be responsible for profound changes observed.
This effect could confirm once again the criticality of linoleic acid. Obviously, the consequences of the condition of excess will focus on the biological system in which the phenomenon occurs.
Even in case of reduction of linoleic acid may occur undesirable phenomena, as happens for example in the process of hibernation, in regulating the flow of calcium into the cardiac cell [87, 88].
The lipid structure of the brain as well as investigated [Cocchi and Noble, data not published, [89]] manifests the same characteristics in the extreme positions of the evolution of warm-blooded animal (from birds to humans) in the course of phylogeny [90], i.e. the level of Linoleic Acid is very low (about 0.3%).
It is possible to assume, reasonably, that while the manifestation of Mood Disorders is recognizable by the increase or decrease of specific fatty acids, Linoleic acid, even in its consolidated stability, could be the element capable of inducing, for small changes, amplifications of pathological brain responses.
Perhaps, within the concept of symmetry breaking and within the considerations on the linoleic acid, we can find answers to questions that the work done raises.
In particular we have, for a long time, faced the problem of how the set of three fatty acids could correspond with absolute precision to a condition of DM or DB. The perception that the set of identified molecular mechanisms might underlie the implications of quantum consciousness has been widely debated, finding aspects of great consistency in the molecular interactions involving membrane Gsα protein and cytoskeleton [9, 29, 28, 91, 92].
If we look at the map of the B2 index and the distance between the indexes (the expression of a molecular properties) we can realize how, in biology, the mathematical measure can express appreciable variations, in a numeric around, relatively close, as well as the positive and negative sign that characterizes respectively DB and DM, can never be interchangeable, consistent with the demonstration of the symmetry breaking between DM and DB.
Spinocerebellar ataxia (SCA), which is included in spinocerebellar degeneration (SCD), is a genetically heterogeneous group of autosomal dominantly inherited progressive disorders [1]. Cerebellar atrophy is the most prominent clinical feature of this condition and is accompanied by spinal cord and sequential brain stem and basal ganglion damage. Therefore, coordinated movement of the eyes, head, trunk, and extremities is impaired. Therefore, the activities of daily living (ADL) and participation in social activities are limited, and the quality of life (QOL) is undisputedly impaired in these patients [2].
The effects of medication and surgery in this clinical setting depend on the cause of ataxia and the extent of neuronal damage [3, 4]; however, there is no rational effective treatment for SCA and it is difficult to slow the progression of the disease. Rehabilitation [5, 6], including physical therapy [7, 8], aimed at improving/maintaining motor function, ADL, and QOL [5] is an important intervention for patients with SCA. Here we provide a narrative review of physical rehabilitation for SCA.
For the clinical diagnosis of cerebellar ataxia, specific blood studies and magnetic resonance imaging (MRI) have been performed [9]. Furthermore, genetic techniques improve the diagnosis of degenerative cerebellar ataxia [10]. Although the details of the findings of these genetic and blood studies are beyond the scope of this review of rehabilitation, cerebellar atrophy and cerebellar motor deficits are traditionally common observations in patients with degenerative cerebellar ataxia [9]. Furthermore, recently, the absence of motor cerebellar symptoms has also been recognized as being important for rehabilitation [11].
The cerebellum is the motor-control system in humans [12]. Clinically, the oculomotor deficit, speech deficits, ataxia in the trunk and extremities, balance disorder, and gait disturbance are the targets of rehabilitation in SCA [9, 13]. The possible underlying pathogenetic mechanisms include distorted timing, abnormal sensory acquisition, impaired sensory motor synchronization, impaired triggering of corticomotor excitability, and abnormal visuokinesthetic cerebro-cerebellar interactions [13].
Oculomotor deficits cause deoptimized vision. The vestibulo-ocular reflex and smooth pursuit [14] partially depend on motor prediction in static and dynamic movement and contribute to dynamic gazing [15]; moreover, the cerebellum contributes to the trainability of eye-head coordinated movements [16].
Abnormal excitability and modulation in the motor cortex and corticospinal tract causes a voluntary contraction deficit in [17, 18]. Cerebellar stimulation modulates the motor-evoked potential induced by transcranial magnetic stimulation (TMS) of the primary motor cortex [19, 20, 21]; however, this modulation is absent in patients with SCA [22, 23]. Furthermore, the cortical silent period, which reflects the excitability of the inhibitory GABAergic neural circuit in the primary motor cortex, is abnormal in these patients [24, 25, 26, 27, 28, 29], and this cerebellar effect on the cortical silent period is characteristic of the healthy population [30]. Before muscle contraction for movement, the corticospinal excitability increases in healthy individuals; in contrast, this facilitation is insufficient in SCA [31]. In addition, in patients with SCA, muscle tones are decreased [11] and the spinal reflex excitability is facilitated by cerebellar stimulation [32, 33, 34]. The long latency spinal reflex, which is correlated with the cortical circuit, is disturbed in SCA [35]. Although this functional cerebellum-spine connection may contribute to the preparation for muscle contraction, there is insufficient evidence that these connections contribute to motor control in healthy and cerebellar ataxia populations.
In simple movements, such as extension of the elbow, coordinated activity of the biceps and triceps is needed. For ballistic elbow-extension movement practice, the triphasic muscle agonist and antagonist contraction patterns contribute to the smooth movement, but under/overshooting appears during the uncoordinated contraction pattern of patients with SCA [36, 37]. Furthermore, this contraction pattern may be obtained by temporal electrical stimulation in these individuals [37].
The cerebellar internal model contributes to predictable/online/offline motor control and motor learning/adaptation [38]. The symptoms associated with motor learning do not appear at the onset of the cerebral atrophy [39], because several brain areas, i.e., the prefrontal cortex, primary motor cortex, and basal ganglia, compensate for cerebellar function in early-stage SCA [5, 6, 39]. Recently, the motor learning deficit at the early stage of the disorder was reportedly detected using an adaptation task [40]. Therefore, the assessment of the capacity for motor learning may be important to strategize the interventions that are concretely described in the following sections.
Representative nonataxia symptoms include hyperreflexia, areflexia, extensor plantar, spasticity, paresis, muscle atrophy, fasciculations, myoclonus, rigidity, chorea/dyskinesia, dystonia, resting tremor, sensory symptoms, urinary dysfunction, cognitive impairment, and brain stem oculomotor signs [41]. The Inventory of NonAtaxia Symptoms (INAS) [41] is used to estimate these nonataxia symptoms. The appearance of these symptoms depends on the type of SCA [41].
We should conduct assessment to detect the degree of motor dysfunction and consider more effective intervention of physical rehabilitation. The first, the imaging technology such as MRI provides us with structural information about the atrophic areas of the brain associated with the disease. We described about neuroimaging technique in Section 3.1. The next, we can use some outcome measurement to estimate the motor dysfunction and verification in the physical rehabilitation. Then, we introduce the representable outcome measures for physical rehabilitation in SCA in Section 3.2. However, we had not established method to estimate the remaining of motor learning ability, which is one of the most important factors to predict the effect of physical rehabilitation. Therefore, we propose the possible assessment of motor learning ability in Section 3.3.
Neuroimaging is a technique that is used to visualize the structural and functional activities of the brain. MRI measurements, such as diffusion tensor imaging and surface-based morphometry, visualize the brain structures. Functional activity imaging is achieved using fMRI and NIRS, which are indicators of cerebral blood flow, and electroencephalogram (EEG) and magnetoencephalography, which are indicators of electrical activity. Positron emission tomography and single-photon emission computed tomography with nuclear tracers are also used in this setting. The application of neuroimaging in the rehabilitation of cerebellar disorders includes voxel-based lesion symptom mapping in patients with stroke, to investigate the recovery of upper arm reach [42] and walking ability [43] depending on the lesion site.
Although conventional MRI [44] is widely used for the neuroimaging of spinocerebellar degeneration, to obtain diagnostic findings, few studies have used neuroimaging as a guideline or outcome of rehabilitation. The lack of reports in this context hampers the quantification of cerebellar degeneration in SCA and its correlation with motor dysfunctions. In terms of measurement techniques, the cerebellum exhibits a much tighter folding compared with the cerebral cortex, with individual cortical sheets with a thickness of 1–2 mm and a sheet area of 1500–2000 cm2, compared with a sheet area of 2200 cm2 with a thickness of 1.5–4 mm in the cerebral cortex. Therefore, the typical 2–4 mm3 spatial resolution of neuroimaging techniques is insufficient to capture local cerebellar changes. Patient factors include the difficulty in limiting the brain regions involved in movement disorders to the cerebellum, because the degenerative regions in SCD extend beyond this structure to multiple brain regions [45].
Among the neuroimaging modalities, the role of voxel-based morphometry (VBM) is notable in SCA rehabilitation. VBM is a statistical analysis of the entire brain in voxel units (1 mm3) that is used to identify the behavioral patterns and related brain morphological characteristics of patients [46]. Burciu et al. assessed the degree of cerebellar atrophy concerning motor and learning functions using VBM to evaluate brain structure changes after 2 weeks of balance training in patients with SCD; these authors reported the association between an increased volume of the dorsal premotor cortex and increased balance ability [47]. Matsgi et al. reported an association between VBM and neurophysiological markers in cerebellar brain inhibition (CBI), with atrophy of the dentate nucleus at VBM observed in cases of pure cerebellar ataxia that did not show CBI [48]. Bando et al. reported a correlation between adaptive learning ability and gray matter volume of the cerebellar IV-VII lobules and the supramarginal gyrus in a prismatic adaptation task in SCA [49]. Thus, VBM may be a biomarker to explain motor dysfunction in patients with SCA.
Conversely, VBM is not an ideal tool to show a causal relationship between brain structural changes and behavioral differences. As a solution to this problem, we can propose a combination of VBM and neurostimulation [50], as neurostimulation of the brain regions associated with the behavioral patterns obtained by VBM and the observation of behavioral changes before and after stimulation allow us to examine brain degeneration sites and behavior.
Gait disturbance is a major symptom of the cerebellar pathology in SCA [51]. The functional ambulation categories (FAC) is useful for the comprehensive assessment of walking ability; the FAC assesses gait for about 15 m and climbing stairs and classifies gait levels into 6 levels [52]. The FAC is also used in the exercise program created by Research Committee for Ataxia Disease (Research team under the jurisdiction of the Ministry of Health, Labour and Welfare in Japan, http://ataxia.umin.ne.jp/rehabilitation/).
The quantitative assessment of cerebellar ataxia is very important in clinical practice. The International Cooperative Ataxia Rating Scale (ICARS) has been used as a quantitative assessment of ataxia symptoms. However, it has been noted that the test reliability of the eye movement items is low [53]. The Scale for Assessment and Rating of Ataxia (SARA) is an 8-item performance-based scale that yields a total score of 0–40 (most severe ataxia). The minimal detectable change (MDC) for individual score difference from the baseline to the 1-year follow-up in SARA was <3.5 (n = 171; SCA1, n = 43; SCA2, n = 61; SCA3, n = 37; and SCA6, n = 30; mean age, 50.9 ± 13.5 years; mean disease duration, 11.8 ± 5.6 years) [54]. SARA does not include an eye movement section. Schmahmann et al. noted the importance of assessing oculomotor abnormalities and developed the Brief Ataxia Rating Scale, a modification of ICARS [55]. Each SCA genotype exhibits specific symptoms [56]. Therefore, these assessments should be used differently for different symptoms. However, one feature that is consistent among these assessments is that the scoring range is large and does not allow the assessment of minute symptom changes. Honda et al. developed a system to measure the evaluation of SARA using a depth sensor [57]. Using this system, the degree of ataxia can be measured numerically. In addition, because the system is inexpensive, it can be installed at the patient’s home, making it a useful tool for telemedicine.
The balance dysfunction in SCA has a significant impact on QOL [58]. The Berg Balance Scale and the Timed Up and Go test are widely used to assess balance dysfunction in SCA [59]. However, despite their widespread use, these assessments have not been examined for reliability and validity in SCA. Kondo et al. examined the test reliability of the Balance Evaluation Systems Test (BESTest) [60]. The BESTest is a multitask balance assessment tool that was developed to identify specific postural control problems (i.e., biomechanical constraints, stability limits, anticipatory postural adjustments, postural responses, sensory orientation, dynamic balance during gait, and cognitive effects) [61]. The MDC for an individual score difference from the baseline to the 4-week follow-up in BESTest was <8.7 (n = 20; SCA3, n = 4; SCA6, n = 9; SCA31, n = 7; mean age, 63.7 ± 10.1 years; age at onset, 53.9 ± 10.5 years; baseline SARA, 9.9 ± 3.5) [61]. Many types of balance function measures have been reported. However, BESTest is the only scale that is considered to have absolute reliability in SCA.
Gait speed is often used as an outcome of intervention studies in SCA [62, 63]. However, some changes in the gait pattern (e.g., base of support and gait speed) most likely reflect cerebellar-unspecific, compensatory strategies, and a high spatiotemporal gait variability appears to be a distinctive feature of ataxic gait [58, 64]. The Gait Variability Index (GVI) is a measure of gait variability that has been examined regarding reliability and validity [65]. The MDC for an individual score difference from day 1 to day 2 in GVI was <8.6 (Friedreich’s ataxia, n = 81; baseline ICARS, 70.4 ± 7.9) [65]. It has been suggested that gait instability in SCA are characterized by a stronger effect of balance-related impairments of cerebellar control during slow walking and a stronger effect of impaired intra-limb coordination during fast walking [58]. Therefore, in clinical practice, it is necessary to evaluate not only the optimal gait speed, but also slow walking and fast walking, to extract the characteristics of gait instability.
The cerebellum has the ability to compensate for tissue damage and loss of function. This is called the cerebellar reserve [6]. Mitoma et al. suggested that this is important for motor rehabilitation at a time when the cerebellar reserve is functioning [6]. Motor rehabilitation in the early stages may maintain and improve the cerebellar reserve [66, 67]. Therefore, it is important to assess this parameter.
Cerebellar ataxia is the main symptom of SCA. Ataxia symptoms may represent a compensation for predictive control using feedback control [6]. Predictive control requires a mechanism called internal model [38]. The internal model is constantly updated by motor learning [68]. In turn, motor learning is one of the most important functions of the cerebellum. Thus, a measure of motor learning ability may be useful as an assessment of the cerebellar reserve.
Prism adaptation (PA) is widely used as an assessment of motor learning ability in patients with SCA [40, 69]. The basic procedure of PA is shown in Figure 1. First, at the “baseline,” the task is performed without a prism lens. Subsequently, the prism lens is introduced and the task is performed. In the initial phase, the lens is set off to either the left or right side of the target, but the error is corrected as the number of repetitions increases. This period is called the “initial error correction phase.” Thereafter, a spatial realignment phase is performed under the prism lens. The purpose of this phase is to gather visuospatial information including the errors. Next, the prism is removed and an “after-effect phase” is performed. If the spatial information is being re-learned, errors are generated in the opposite direction to the initial error correction phase. Recently, Hashimoto et al. developed the Adaptability Index (AI), which is a composite index computed from several parameters measured PA (Figure 2). The clinical efficacy of the AI in discriminating patients with SCA from healthy individuals has been demonstrated [70]. Furthermore, Bando et al. found that a reduced AI was correlated with gray matter atrophy in the cerebellum in the SCA group [49]. In particular, the right lobule VI and the left Crus I showed the most robust correlation. These cerebellar regions are consistent with the correlates of PA detected in previous human and nonhuman primate studies [71, 72]. AI is considered as a motor learning index that reflects the cerebellar reserve (in this case, the degree of cerebellar atrophy).
Overview of prism adaptation. The ordinate shows the finger-touch error represented from the target to the touch point. Three phases are generally used: (1) absence of a prism lens (prism off), (2) presence of a prism lens (prism on), and (3) absence of a prism lens (prism off).
Calculation of the adaptability index (AI). The AI is calculated as follows: AI = a × b × c, where “a” is the adaptation index defined as the probability of correct touches in the last 10 trials of the spatial realignment phase 1, “b” is the retention index defined as the probability of incorrect touches in the initial 5 trials of the after-effect phase, and “c” is the extinction index designated as the probability of correct touches in the last 10 trials of the spatial realignment phase 2.
PA can be implemented using a simple system. In addition, it takes only 20 min to complete a PA. Reaching tasks can be performed even in the period during which the patient is unable to walk, and the fact that the PA can be assessed continuously over a long period is an advantage. However, only cross-sectional studies have been conducted in previous reports [40, 49, 69, 70, 73, 74]. Future studies need to be designed to examine long-term changes and intervention effects.
The targets of rehabilitation in cerebellar ataxia are mainly disability in ADL, gait, and motor dysfunction. Therefore, GAS, FIM, 10-m walking test, TCA, SARA, ICARS, and BESTest are used as important outcomes in rehabilitation. The most important strategies of rehabilitation for cerebellar ataxia including SCA consists in balance training (see Section 4.3), gait training (see Section 4.2), and muscle strengthening training using a high-intensity program (see Section 4.1). Further, optional possible interventions are using assistive technology (see Section 4.4) and neuromodulation technique (see Section 4.5).
Rehabilitation methods for cerebellar ataxia have been reported [75]. The most important strategy is the increase in the intensity of physical training, such as balancing, gait, and strength [76]. Several systematic reviews [77, 78, 79] and narrative reviews [3, 75, 80, 81] introduced and recommended intensive physical therapy for cerebellar ataxia in patients with SCA. Miyai et al. [62] reported that physical and occupational therapies of 2 h × 5 days +1 h × 2 days per week for 4 weeks were applied to inpatients and improved the SARA score and gait speed; however, the effect was carried over only up to 12 weeks after the training, and had disappeared at 24 weeks [62]. Conversely, Ilg et al. reported that intensive coordinative physiotherapy delivered over 4 weeks improved motor performance in degenerative cerebellar ataxia in a study with an intraindividual control design [63].
An outpatient rehabilitation program for 6 weeks applied to 19 participants with Friedreich’s ataxia improved the motor domain item in the FIM score and Friedreich’s Ataxia Impact Scale, but the posthome program could not maintain the effect [82]. Therefore, this finding indicates that continuous outpatient rehabilitation programs are important for maintaining the ADL in patients with Friedreich’s ataxia. Additional large-scale studies are needed to investigate the long-term effect of outpatient rehabilitation programs and identify the characteristics of patients who respond to treatment. Therefore, the development of optimal individual programs is important to obtain the effect of training, regardless of the inpatient, outpatient, or home-self-training setting [83]. The semi-order program of the Research Committee for Ataxia Disease (Research team under the jurisdiction of the Ministry of Health, Labour and Welfare in Japan, http://ataxia.umin.ne.jp/rehabilitation/) can be used for this purpose.
Subsequently, the continuity of the intensive training is an important factor, because degradation in physical function was reported. Therefore, approaches aimed at upkeeping these programs in a way that suits the patients are needed. For example, exergames contribute to the practice of exercise at home. In the future, tele-rehabilitation systems [84] should be tested for the improvement (or maintenance) of the function and continuity of exercise.
Gait training has been reported to improve spatiotemporal gait parameters (cadence, step length/width, gait speed, etc.) [85, 86, 87], complex gait (Timed Up and Go test, Dynamic Gait Index) [85], independence (FAC) [86], ataxia (SARA) [88], and adaptive locomotor adjustments (ALA) [88]. Patients with SCA exhibit problems other than the gait disturbance itself, i.e., stiffening of the body in an attempt to avoid the occurrence of gait disturbances. Therefore, it is important to focus on gait disturbances and increasing the number of walking patterns when considering gait training in a person with SCA.
Disturbances of gait are the core features of SCA [89, 90, 91, 92], thus leading to a risk of falling down [93]. Patients with cerebellar ataxia walk with a reduced walking speed and cadence, as well as reduced step length, stride length, and swing phase; increased walking base width, stride time, step time, stance phase, and double limb support phase; and increased variability of step length, stride length, and stride time [94]. These items are affected by both balance-related impairments and deficits related to limb control and intra-limb coordination [95]. We believe that balance training and coordination training are key to the improvement of gait disturbances. Regarding the details of balance training, please refer to the Section 4.3.
In addition, stiffening of the body leads to a decrease in the number of walking patterns; as a result, ALA deteriorates [96, 97]. ALA implies that obstacle avoidance is achieved by modifying basic walking patterns in response to obstacle properties, e.g., a sloping road, stepping over an obstacle, or dynamically changing the spaces created by pedestrians in a hallway. In persons with SCA, feelings of anxiety as a result of the frequent experience of falls, as well as deficits related to limb control by ataxia, could negatively affect their ALA because of increased muscular co-contractions and reduced joint movements [98]. We will describe the approaches to improve ALA in the next paragraph.
The proposals for gait training are as follows: gait training without or with a treadmill. First, in gait training without a treadmill, we refer the reader to Section VI of the BESTest as gait adaptability training [61]. Section VI of the BESTest consists of a 7-item scale: (1) Gait Natural, (2) Change Speed, (3) Head Turns, (4) Pivot Turn, (5) Obstacles, (6) “Get Up & Go” Test, and (7) Cognitive Task “Get Up & Go” Test, aimed at evaluating the stability of the gait. These elements are important to improve ALA. As an example of gait training, persons with SCA are asked to walk while making an effort to change their walking speed according to therapist’s instructions to engage is “fast (or slow)” walking as fast (or slow) as possible. If patients need assistance when walking, you might want to change the walking speed with the support of a therapist.
Second, gait training using a treadmill has advantages in that patients can practice a relatively large amount of gait training over a short period and the therapists can control the speed and incline easily. Gait training using a treadmill has been reported as a potentially promising tool for improving ALA in a person with SCA [88], as well as gait disturbances in a person with Parkinson’s disease [99, 100]. It has been reported that variability was increased during slow and fast walking, but was normal during the preferred walking speed in a person with cerebellar ataxia [101]. Another study reported that, in ataxia, walking at the preferred speed minimizes the gait abnormalities, and the analysis of gait at a wide range of speeds is recommended [94]. For this reason, when using a treadmill in gait training, we suggest that walking be practiced at the speed at which the gait disturbance increases (i.e., slow or fast walking speed) for specific patients. When the fear of falling increases, the use of a harness is recommended, to provide a safe environment for gait without the fear of falling.
It is important to improve the balance ability and ALA during gait training in a person with SCA. Gait training is a relatively easy method; however, it is left to the therapist’s discretion and experience. By changing the task itself or adjusting the difficulty level of the task, gait training may be able to overcome the limited walking patterns of these patients.
All patients with SCA will develop balance difficulties during the course of the disease. Balance is essential for mobility, and is very important for QOL. Although there is no effective pharmacological treatment for decreasing the ataxia or slowing disease progression, physical therapy plays an important role in controlling ataxia and improving or maintaining function through training [76]. In general, the physical therapy programs for degenerative cerebellar ataxia are based on intensive static and dynamic balance and coordination training. There is some evidence that such therapeutic training programs alleviate the ataxic symptoms and improve functional activities in a person with cerebellar ataxia [63, 78, 102]. In these patients, the disease progressively damages the cerebellar structure that plays a crucial role in motor learning [103]; however, these studies have indicated that it is necessary for highly repetitive balance training for balance impairment in SCA. For this reason, highly repetitive balance training in patients with SCA should be the focus of future studies.
More concretely, balance training exercises in early stages of the disease, i.e., ambulation, include the following categories: (1) static balance training, (2) dynamic balance training, and (3) coordination training (Figure 3). In addition, combining a dual task with balance training improves balance and reduces the number of falls in individuals with cerebellar ataxia [104].
National Center of Neurology and Psychiatry (NCNP) balance training program. This balance training program was devised through consultations with patients with SCA, medical doctors, and therapists at the NCNP in Japan. In the advanced stage of SCA, it is recommended to perform the programs indicated by an asterisk.
Moreover, it is important to provide support for these approaches and make them a habit of exercising. For instance, if the patients with SCA have no habit of exercising, they should start with a small number of exercises (i.e., the minimum necessary) to get used to exercising, followed by the gradual increase in the number of exercises. If the patients with SCA have a habit of exercising, the therapist should teach them to adjust the exercise load (e.g., exercise more slowly and/or provide a small base of support). It is also important to adopt balance training that can be enjoyed, e.g., video games [105] and Tai Chi [106], as a means of continuing balance training.
In advanced stages of the disease (i.e., no ambulation), it is necessary to perform balance training under safe conditions (e.g., prone, supine, crawl, and sitting positions), to prevent the decrease in physical activity. Even in advanced stages, it has been reported that a person with degenerative ataxia may benefit from balance training [107]. In addition, it is necessary to focus on ADL and living infrastructure at this stage. If a patient with SCA requires assistance during transfer, engaging in repetitive transfer training with assistance and/or modification of the living infrastructure (e.g., installation of handrails) are necessary.
Focusing on highly repetitive balance training in patients with SCA might preserve the balance function. There is no scientific basis for the number of balance training exercises that are necessary to achieve this goal; however, we would like to recommend engaging in 30 repetitions at least per balance training session. Furthermore, the balance training must be designed to provide a significant challenge to the person’s balance. If a person with SCA wants to preserve the balance function, they have to continue engaging in repetitive balance training, “use it or lose it.” However, few studies have reported the effect of gait and balance training in persons with SCA. Therefore, further studies are needed to clarify the clinical effectiveness of gait and/or balance training.
In recent years, various technologies have been used in the assessment of and treatment based on rehabilitation, as well as to support daily life in patients with SCD. Curara, a wearable robotic system, assists both hip and knee movements and supports the wearer’s rhythmic gait using a synchronization control based on a central pattern generator [108]. Gait support using the curara system has been reported to improve gait smoothness in patients with SCD [109]. In addition to these findings, a recent study addressed the effects of robotic gait training combined with noninvasive brain stimulation. This report showed that robot gait training using Lokomat-Pro in combination with cerebellar tDCS improved the functional scores on SARA, especially the scores on the subitems of gait, stance, sitting, and heel-shin slide compared with robot gait training alone [110]. Thus, hybrid training using robots and noninvasive brain stimulation will be applied to the rehabilitation treatment of patients with SCD in the future.
Accordingly, the use of walking aids is a complementary method for balance and gait impairment. In general, walking aids such as canes and walkers improve postural stability, but their improper use increases the risk of falling [111]. Because the manipulation of a cane requires coordinated upper limb movements [112], patients with SCD who have upper limb ataxia are likely to experience difficulty in using a cane. Conversely, because a walker does not require much coordinated movement of the upper limbs, technology-based walkers are being developed. Recently, a smart walker for mobility assistance and monitoring system aid, ASBGo, was developed and reported to improve gait parameters and postural stability in patients with SCA [113, 114]. In addition to technology, some studies on walking assistance using dogs and handkerchiefs have also been reported. Walking with a rehabilitation dog that has been specifically trained for goal-directed interventions or with an assistance dog that helps people with physical disability and mobility impairments has been reported to improve balance while walking in patients with SCD [115]. Furthermore, the handkerchief-guided gait, in which the patient with SCD walks along with the caregiver while maintaining light tension on a handkerchief by pulling lightly, has been shown to decrease body swaying and increase stride length and gait velocity during walking [116].
Moreover, technology is also being used as a tool to assess ataxia in patients with SCD living at home. Most of them represent attempts to evaluate SARA, which is a typical measure of ataxia, at home. In recent years, a technology aimed at objectively evaluating the speech, upper and lower limb, balance, and gait functions using wearable inertial sensors and a Kinect camera was developed, which makes it possible to discriminate between normal and abnormal functions and to detect ataxia at an early stage [117]. In addition, SaraHome has been developed to allow the remote evaluation of SARA items using Kinect and Leap Motion Controller [118]. Moreover, a spoon equipped with an inertial sensor, called Ataxia Instrumented Measurement-Spoon, has been developed, which allows the evaluation of upper limb function in ataxia while eating with a spoon [119, 120, 121]. Because SCD is an intractable neurological disease, it is difficult for many patients to leave their houses. Therefore, the contribution of technology to home-based rehabilitation is expected to increase in the future if a low-cost and easy method of assessing ataxia at home is established using the technologies and products of daily living described above.
Regarding the support of ADL, BMI studies have been reported. Patients with severe SCA often have difficulty in communicating because of language impairment. The application of BMI using event-related potentials and frequency bands of EEG is being investigated as a solution to this problem. The operational accuracy of BMI using P300 for event-related potentials was 82.9% in patients with SCA, which was similar to the accuracy observed in healthy subjects (83.2%) [122]. There are also reports of BMI manipulation in patients with SCD using the EEG frequency band associated with motor imagery [123]. BMI has a wide range of applications in diseases of the central nervous system, such as communication tools, transportation, and life support, and is expected to contribute to the QOL of patients with SCD.
Neuromodulation via noninvasive brain stimulation (NIBS) is a potential method for the treatment of cerebellar ataxia [19, 124]. A previous systematic review [125] reported the effectiveness of cerebellar neuromodulation using the TMS technique of transcranial direct current stimulation (tDCS). The SARA and ICARS scores in patients with SCA3, multiple system atrophy, and postlesion ataxia, as assessed using real cerebellar rTMS (1 Hz), were significantly lower than those detected in the sham stimulation group [125]. Furthermore, no harmful side effects were noted [125]. Cerebellar rTMS can modulate the plasticity of the vestibular reflex [16, 126]; therefore, cerebellar rTMS has potential for application in balance training to enhance vestibular contributions.
A single session of anodal cerebellar tDCS (2 mA, 20 min) significantly improved SARA, ICARS, 9-hole-peg test, and 8-m walking test scores [127]. Furthermore, combined anodal cerebellar tDCS and cathodal spinal DCS (5 days/week, 2 weeks) improved SARA score, ICARS score, 9-peg test, and 8-m walking time in patients with degenerative cerebellar ataxia [128]. There is insufficient evidence regarding whether simultaneous stimulation is more effective than single stimulation [129]; however, it is possible that this intervention method will produce improvements. Based on these findings, which were gleaned from small-sample studies, we suggest that a neuromodulation montage will improve the ataxia, balance, and gait ability. Therefore, we should perform further studies using a larger population.
Individualized physical rehabilitation programs for patients with SCA may improve/maintain their motor function, balance, gait ability, and ADL. In particular, the intensity and continuity of gait and balance training need to be considered to achieve effectiveness. Furthermore, several technologies, such as depth sensors, robotics, and NIBS, have contributed to the development of methods for the assessment and treatment of motor dysfunction in individuals with SCA. We should continue to study populations suffering from dysfunction caused by SCA.
This work was supported by Shijonawate Gakuen University and JSPS KAKENHI (Grant Number 20 K11298).
The authors declare no conflict of interest.
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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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