Epileptogenesis in rodents leads to neural system dysfunction and loss of associative memory measured by auditory event related potentials.

Epilepsy is a common and disabling neurological condition affecting 1-2% of the world’s population. Individuals suffering from epilepsy are prone to cognitive dysfunctions with detrimental effects in neural processing and memory resulting in decreases in quality of life. An evaluation of inherent neural processes is valuable information to diagnose and clinically assess cognitive function, which could significantly improve the treatment possibilities and thereby the quality of life for epilepsy patients. An evaluation of cognitive functions during epileptogenesis was performed by experiments using auditory event related potentials (ERP) in rats before and after induction of status epilepticus (SE) using the Lithium-Pilocarpine model (LP) of epilepsy. The aim of this study was to assess changes in neural system function during epileptogenesis by evaluating inherent responses to auditory stimuli in three ERP tasks at different time periods: before SE (control state), one week-, one month- and two months- after SE (epileptic state). 1. Habituation- (a.) evaluate the ability to habituate to repeated auditory stimuli using the N70 peak response, (b.) examine the time-frequency response through inter-trial coherence (ITC) and event-related spectral perturbation (ERSP); 2. Chirp- evaluate the auditory steady state responses through ITC; and, 3. Mismatch-Negativity (MMN)- evaluate associative memory through ERP responses to regular or odd tones. Habituation tasks showed increased N70 peak magnitude during epileptogenesis from 1-week, 1-month, and 2-months after SE using repeated measures analysis of variance (rANOVA) with significant differences before and after SE (p\u3c0.05, 1-week, 2-months). ITC showed significant differences between groups during habituation from 0.5-20 Hz and ERSP from 60-100 Hz and 0.5-15 Hz, with baseline corrected ERSP revealing differences from 1-30 Hz. The habituation results indicate a diminished ability to properly habituate to repeated stimuli with abnormal neuronal firing in the epileptic state compared to the non-epileptic control state linking a possible mechanism with imbalances in neuronal inhibition and excitation during epileptogenesis. Chirp response ITC showed increased synchronous activity in high gamma band (\u3e40 Hz) during epileptogenesis indicating the neuronal response in epileptic groups are phase locked to the chirp stimuli at a higher incidence than controls. Epileptic MMN ERP responses for odd and regular tones exhibited a decrease in the response curves from 250-350ms post-stimulus indicating a loss of ability to distinguish tones and difficulties with their associative memory during epileptogenesis.Our results indicate that a proper EEG-based analysis of auditory ERPs are useful in evaluating neural systems during epileptogenesis showing clear imbalances in excitatory: inhibitory function, as well as an indication that associative memory is detrimentally affected. The ERP methods employed may help in the diagnosis of the epileptic patients with cognitive disabilities as their epilepsy progresses, as it is simple, non-invasive and cost effective

Reduction in Inter-Hemispheric Connectivity in Disorders of Consciousness

Clinical diagnosis of disorders of consciousness (DOC) caused by brain injury poses great challenges since patients are often behaviorally unresponsive. A promising new approach towards objective DOC diagnosis may be offered by the analysis of ultra-slow (<0.1 Hz) spontaneous brain activity fluctuations measured with functional magnetic resonance imaging (fMRI) during the resting-state. Previous work has shown reduced functional connectivity within the “default network”, a subset of regions known to be deactivated during engaging tasks, which correlated with the degree of consciousness impairment. However, it remains unclear whether the breakdown of connectivity is restricted to the “default network”, and to what degree changes in functional connectivity can be observed at the single subject level. Here, we analyzed resting-state inter-hemispheric connectivity in three homotopic regions of interest, which could reliably be identified based on distinct anatomical landmarks, and were part of the “Extrinsic” (externally oriented, task positive) network (pre- and postcentral gyrus, and intraparietal sulcus). Resting-state fMRI data were acquired for a group of 11 healthy subjects and 8 DOC patients. At the group level, our results indicate decreased inter-hemispheric functional connectivity in subjects with impaired awareness as compared to subjects with intact awareness. Individual connectivity scores significantly correlated with the degree of consciousness. Furthermore, a single-case statistic indicated a significant deviation from the healthy sample in 5/8 patients. Importantly, of the three patients whose connectivity indices were comparable to the healthy sample, one was diagnosed as locked-in. Taken together, our results further highlight the clinical potential of resting-state connectivity analysis and might guide the way towards a connectivity measure complementing existing DOC diagnosis

Interfacce cervello-computer per la comunicazione aumentativa: algoritmi asincroni e adattativi e validazione con utenti finali

This thesis aimed at addressing some of the issues that, at the state of the art, avoid the P300-based brain computer interface (BCI) systems to move from research laboratories to end users’ home. An innovative asynchronous classifier has been defined and validated. It relies on the introduction of a set of thresholds in the classifier, and such thresholds have been assessed considering the distributions of score values relating to target, non-target stimuli and epochs of voluntary no-control. With the asynchronous classifier, a P300-based BCI system can adapt its speed to the current state of the user and can automatically suspend the control when the user diverts his attention from the stimulation interface. Since EEG signals are non-stationary and show inherent variability, in order to make long-term use of BCI possible, it is important to track changes in ongoing EEG activity and to adapt BCI model parameters accordingly. To this aim, the asynchronous classifier has been subsequently improved by introducing a self-calibration algorithm for the continuous and unsupervised recalibration of the subjective control parameters. Finally an index for the online monitoring of the EEG quality has been defined and validated in order to detect potential problems and system failures. This thesis ends with the description of a translational work involving end users (people with amyotrophic lateral sclerosis-ALS). Focusing on the concepts of the user centered design approach, the phases relating to the design, the development and the validation of an innovative assistive device have been described. The proposed assistive technology (AT) has been specifically designed to meet the needs of people with ALS during the different phases of the disease (i.e. the degree of motor abilities impairment). Indeed, the AT can be accessed with several input devices either conventional (mouse, touchscreen) or alterative (switches, headtracker) up to a P300-based BCI.Questa tesi affronta alcune delle problematiche che, allo stato dell'arte, limitano l'usabilità delle interfacce cervello computer (Brain Computer Interface - BCI) al di fuori del contesto sperimentale. E' stato inizialmente definito e validato un classificatore asincrono. Quest'ultimo basa il suo funzionamento sull'inserimento di un set di soglie all'interno del classificatore. Queste soglie vengono definite considerando le distribuzioni dei valori di score relativi agli stimoli target e non-target e alle epoche EEG in cui il soggetto non intendeva effettuare nessuna selezione (no-control). Con il classificatore asincrono, un BCI basato su potenziali P300 può adattare la sua velocità allo stato corrente dell'utente e sospendere automaticamente il controllo quando l'utente non presta attenzione alla stimolazione. Dal momento che i segnali EEG sono non-stazionari e mostrano una variabilità intrinseca, al fine di rendere possibile l'utilizzo dei sistemi BCI sul lungo periodo, è importante rilevare i cambiamenti dell'attività EEG e adattare di conseguenza i parametri del classificatore. A questo scopo, il classificatore asincrono è stato successivamente migliorato introducendo un algoritmo di autocalibrazione per la continua e non supervisionata ricalibrazione dei parametri di controllo soggettivi. Infine è stato definito e validato un indice per monitorare on-line la qualità del segnale EEG, in modo da rilevare potenziali problemi e malfunzionamenti del sistema. Questa tesi si conclude con la descrizione di un lavoro che ha coinvolto gli utenti finali (persone affette da sclerosi laterale amiotrofica-SLA). In particolare, basandosi sui principi dell’user-centered design, sono state descritte le fasi relative alla progettazione, sviluppo e validazione di una tecnologia assistiva (TA) innovativa. La TA è stata specificamente progettata per rispondere alla esigenze delle persone affetta da SLA durante le diverse fasi della malattia. Infatti, la TA proposta può essere utilizzata sia mediante dispositivi d’input tradizionali (mouse, tastiera) che alternativi (bottoni, headtracker) fino ad arrivare ad un BCI basato su potenziali P300

NEW FRONTIERS IN THE COGNITIVE ASSESSMENT OF AMYOTROPHIC LATERAL SCLEROSIS: BRAIN COMPUTER INTERFACE AND EYE TRACKING

Background: Over the last 20 years, cognitive and behavioural alterations in amyotrophic lateral sclerosis (ALS) have been recognized as an integral part of the disease. A proportion of patients present with a full-blown frontotemporal dementia syndrome, while selective cognitive changes are more commonly found, especially regarding frontal-executive abilities. Moreover, recent studies have highlighted a broader cognitive involvement in this population, concerning language and social cognition. Despite the increased awareness of ALS as a multisystem disorder and the importance of an accurate cognitive evaluation of these patients, the traditional neuropsychological \u2018paper and pencil\u2019 tools do not compensate for patients\u2019 physical disability and can not be adequately used in the moderate-advanced stages of the disease. Objective: To investigate the use of P300-based Brain Computer Interface (BCI) and Eye Tracking (ET) technology for the administration of motor-verbal free cognitive measures in ALS. Materials and Methods: 34 patients diagnosed with ALS and 30 healthy subjects have been recruited. All participants underwent the BCI and ET-based neuropsychological assessment, together with three traditional cognitive screening tools (Frontal Assessment Battery - FAB; Montreal Cognitive Assessment \u2013 MoCA; Working Memory subtest of the Brief Assessment of Cognition in Schizophrenia), two psychological questionnaires (Beck Depression Inventory - BDI; State-Trate Anxiety Inventory - STAI-Y) and a usability questionnaire. For patients, also respiratory examination was performed, and the Frontal Behavioural Inventory - FBI was carried out with caregivers. Results: Significant correlations were observed between the traditional cognitive measures and the BCI- and ET-based neuropsychological assessment, mainly concerning accuracy and time-related variables in the ALS patients sample. Patients provided comparable rates than controls with regard to the BCI and ET usability. Conclusions: The developed motor-verbal free neuropsychological battery allows a longitudinal cognitive assessment during the course of the disease, also when traditional measures are not fully administrable, providing relevant information for clinical practice and ethical issues. Further work will be aimed at refining the developed system and enlarging the cognitive spectrum investigated

Ensemble of classifiers based data fusion of EEG and MRI for diagnosis of neurodegenerative disorders

The prevalence of Alzheimer\u27s disease (AD), Parkinson\u27s disease (PD), and mild cognitive impairment (MCI) are rising at an alarming rate as the average age of the population increases, especially in developing nations. The efficacy of the new medical treatments critically depends on the ability to diagnose these diseases at the earliest stages. To facilitate the availability of early diagnosis in community hospitals, an accurate, inexpensive, and noninvasive diagnostic tool must be made available. As biomarkers, the event related potentials (ERP) of the electroencephalogram (EEG) - which has previously shown promise in automated diagnosis - in addition to volumetric magnetic resonance imaging (MRI), are relatively low cost and readily available tools that can be used as an automated diagnosis tool. 16-electrode EEG data were collected from 175 subjects afflicted with Alzheimer\u27s disease, Parkinson\u27s disease, mild cognitive impairment, as well as non-disease (normal control) subjects. T2 weighted MRI volumetric data were also collected from 161 of these subjects. Feature extraction methods were used to separate diagnostic information from the raw data. The EEG signals were decomposed using the discrete wavelet transform in order to isolate informative frequency bands. The MR images were processed through segmentation software to provide volumetric data of various brain regions in order to quantize potential brain tissue atrophy. Both of these data sources were utilized in a pattern recognition based classification algorithm to serve as a diagnostic tool for Alzheimer\u27s and Parkinson\u27s disease. Support vector machine and multilayer perceptron classifiers were used to create a classification algorithm trained with the EEG and MRI data. Extracted features were used to train individual classifiers, each learning a particular subset of the training data, whose decisions were combined using decision level fusion. Additionally, a severity analysis was performed to diagnose between various stages of AD as well as a cognitively normal state. The study found that EEG and MRI data hold complimentary information for the diagnosis of AD as well as PD. The use of both data types with a decision level fusion improves diagnostic accuracy over the diagnostic accuracy of each individual data source. In the case of AD only diagnosis, ERP data only provided a 78% diagnostic performance, MRI alone was 89% and ERP and MRI combined was 94%. For PD only diagnosis, ERP only performance was 67%, MRI only was 70%, and combined performance was 78%. MCI only diagnosis exhibited a similar effect with a 71% ERP performance, 82% MRI performance, and 85% combined performance. Diagnosis among three subject groups showed the same trend. For PD, AD, and normal diagnosis ERP only performance was 43%, MRI only was 66%, and combined performance was 71%. The severity analysis for mild AD, severe AD, and normal subjects showed the same combined effect

Augmentation of Brain Function: Facts, Fiction and Controversy. Volume III: From Clinical Applications to Ethical Issues and Futuristic Ideas

The final volume in this tripartite series on Brain Augmentation is entitled “From Clinical Applications to Ethical Issues and Futuristic Ideas”. Many of the articles within this volume deal with translational efforts taking the results of experiments on laboratory animals and applying them to humans. In many cases, these interventions are intended to help people with disabilities in such a way so as to either restore or extend brain function. Traditionally, therapies in brain augmentation have included electrical and pharmacological techniques. In contrast, some of the techniques discussed in this volume add specificity by targeting select neural populations. This approach opens the door to where and how to promote the best interventions. Along the way, results have empowered the medical profession by expanding their understanding of brain function. Articles in this volume relate novel clinical solutions for a host of neurological and psychiatric conditions such as stroke, Parkinson’s disease, Huntington’s disease, epilepsy, dementia, Alzheimer’s disease, autism spectrum disorders (ASD), traumatic brain injury, and disorders of consciousness. In disease, symptoms and signs denote a departure from normal function. Brain augmentation has now been used to target both the core symptoms that provide specificity in the diagnosis of a disease, as well as other constitutional symptoms that may greatly handicap the individual. The volume provides a report on the use of repetitive transcranial magnetic stimulation (rTMS) in ASD with reported improvements of core deficits (i.e., executive functions). TMS in this regard departs from the present-day trend towards symptomatic treatment that leaves unaltered the root cause of the condition. In diseases, such as schizophrenia, brain augmentation approaches hold promise to avoid lengthy pharmacological interventions that are usually riddled with side effects or those with limiting returns as in the case of Parkinson’s disease. Brain stimulation can also be used to treat auditory verbal hallucination, visuospatial (hemispatial) neglect, and pain in patients suffering from multiple sclerosis. The brain acts as a telecommunication transceiver wherein different bandwidth of frequencies (brainwave oscillations) transmit information. Their baseline levels correlate with certain behavioral states. The proper integration of brain oscillations provides for the phenomenon of binding and central coherence. Brain augmentation may foster the normalization of brain oscillations in nervous system disorders. These techniques hold the promise of being applied remotely (under the supervision of medical personnel), thus overcoming the obstacle of travel in order to obtain healthcare. At present, traditional thinking would argue the possibility of synergism among different modalities of brain augmentation as a way of increasing their overall effectiveness and improving therapeutic selectivity. Thinking outside of the box would also provide for the implementation of brain-to-brain interfaces where techniques, proper to artificial intelligence, could allow us to surpass the limits of natural selection or enable communications between several individual brains sharing memories, or even a global brain capable of self-organization. Not all brains are created equal. Brain stimulation studies suggest large individual variability in response that may affect overall recovery/treatment, or modify desired effects of a given intervention. The subject’s age, gender, hormonal levels may affect an individual’s cortical excitability. In addition, this volume discusses the role of social interactions in the operations of augmenting technologies. Finally, augmenting methods could be applied to modulate consciousness, even though its neural mechanisms are poorly understood. Finally, this volume should be taken as a debate on social, moral and ethical issues on neurotechnologies. Brain enhancement may transform the individual into someone or something else. These techniques bypass the usual routes of accommodation to environmental exigencies that exalted our personal fortitude: learning, exercising, and diet. This will allow humans to preselect desired characteristics and realize consequent rewards without having to overcome adversity through more laborious means. The concern is that humans may be playing God, and the possibility of an expanding gap in social equity where brain enhancements may be selectively available to the wealthier individuals. These issues are discussed by a number of articles in this volume. Also discussed are the relationship between the diminishment and enhancement following the application of brain-augmenting technologies, the problem of “mind control” with BMI technologies, free will the duty to use cognitive enhancers in high-responsibility professions, determining the population of people in need of brain enhancement, informed public policy, cognitive biases, and the hype caused by the development of brain- augmenting approaches

Neural-heart interactions in the healthy and injured brain

Introduction Integrating internal and external signals is fundamental for perceiving and interacting with the world via the body. In particular, interoceptive predictive coding frameworks describe these integrated mechanisms as vital for embodied selfhood, emotional experience, and a unified first-person perspective. By definition, a disorder of consciousness patient has dysfunctional awareness of their self and their environment. Despite the dual diagnostic criteria, research has focused almost exclusively on external perceptual awareness, leaving internal self-related aspects of awareness largely unexplored. Thus, we sought to detect neural markers of self-related interoceptive processing with the aim that their detection may predict the recovery of self-awareness in acute unresponsive disorders of consciousness. Experiment one First, we aimed to identify neural markers of interoceptive (i.e., cardiac) and exteroceptive (i.e., auditory) integration in healthy individuals. We presented sequences of sounds at a short delay (i.e., perceived synchronous) or long delay (i.e., perceived asynchronous) from the heartbeat, with half the trials including an omission. We analysed heart-evoked potentials (i.e., HEPs) during omissions to measure pure predictive mechanisms without contaminating auditory responses. Pre-omission HEP responses differed across short delay and long delay trials, potentially reflecting differences in heartbeat-driven expectations of sounds. Furthermore, attending to internal heartbeat sensations modulated omission-evoked responses, supporting the role of attentional-precision in regulating cross-modal predictive mechanisms (i.e., state precision). However, we did not observe modulation of HEP/omission-evoked responses by individual difference in interoceptive ability, which doesn't support the proposed regulating role of trait precision in predictive coding frameworks. Therefore, HEP mechanisms of interoceptive and exteroceptive integration operate partially under interoceptive predictive coding. However, we observed inconsistent evidence of modulation by precision-weighting. Experiment two Second, we sought to determine whether the lack of observed trait precision modulation (i.e., by interoceptive ability) and, therefore, inconsistency with precision- weighting resulted from individual differences in the perceived timing of heartbeat sensations. Thus, in experiment two, we tailored the perceived synchronous cardio-audio delays to each individual to test the influence of trait precision more sensitively. Despite this, we observed no significant modulation of HEPs by state or trait precision. Nonetheless, we replicated the robust HEP effect indicative of cardio-audio expectation. Thus, overall, our findings are inconsistent with a precision-weighted predictive coding view. However, it could be that participants relied less on attentional/state precision under a more individually-tailored task. Furthermore, assessing interoceptive ability is challenging, and thus, our interoceptive performance measures may not accurately reflect trait precision. Experiment three Finally, cortical processing of heartbeats at rest is thought to index self-related aspects of awareness, such as embodied selfhood and the formation of a first-person perspective. Hence, we investigated the prognostic potential of resting HEPs and cardiac measures in acute unresponsive patients. We observed no convincing evidence of HEPs or cardiac measures predicting recovery from acute unresponsiveness, three or six months post-assessment. This lack of evidence suggests resting HEPs are not useful for consciousness prognoses. However, greater prognostic value may be found in HEPs during high-level self-processing or interoceptive-exteroceptive integration (i.e., Experiments one and two). Discussion In summary, we observed robust HEP evidence of interoceptive signals guiding expectations of exteroceptive stimuli. However, we observed inconsistent evidence of modulation of HEPs by state precision and no evidence of modulation by trait precision. Thus, we need more explicit definitions of the manipulation and measurement of precision in predictive coding frameworks to test their influence on interoceptive predictive mechanisms accurately. Finally, although previous evidence indicated the diagnostic value of HEPs, we observed no convincing evidence of their prognostic potential. It is possible that during rest, self-cognitive mechanisms reflected in HEPs are reduced. Therefore, investigating HEPs during tasks involving high-level self-processing or interoceptive-exteroceptive integration may be more valuable for awareness prognoses

Decision-based data fusion of complementary features for the early diagnosis of Alzheimer\u27s disease

As the average life expectancy increases, particularly in developing countries, the prevalence of Alzheimer\u27s disease (AD), which is the most common form of dementia worldwide, has increased dramatically. As there is no cure to stop or reverse the effects of AD, the early diagnosis and detection is of utmost concern. Recent pharmacological advances have shown the ability to slow the progression of AD; however, the efficacy of these treatments is dependent on the ability to detect the disease at the earliest stage possible. Many patients are limited to small community clinics, by geographic and/or financial constraints. Making diagnosis possible at these clinics through an accurate, inexpensive, and noninvasive tool is of great interest. Many tools have been shown to be effective at the early diagnosis of AD. Three in particular are focused upon in this study: event-related potentials (ERPs) in electroencephalogram (EEG) recordings, magnetic resonance imaging (MRI), as well as positron emission tomography (PET). These biomarkers have been shown to contain diagnostically useful information regarding the development of AD in an individual. The combination of these biomarkers, if they provide complementary information, can boost overall diagnostic accuracy of an automated system. EEG data acquired from an auditory oddball paradigm, along with volumetric T2 weighted MRI data and PET imagery representative of metabolic glucose activity in the brain was collected from a cohort of 447 patients, along with other biomarkers and metrics relating to neurodegenerative disease. This study in particular focuses on AD versus control diagnostic ability from the cohort, in addition to AD severity analysis. An assortment of feature extraction methods were employed to extract diagnostically relevant information from raw data. EEG signals were decomposed into frequency bands of interest hrough the discrete wavelet transform (DWT). MRI images were reprocessed to provide volumetric representations of specific regions of interest in the cranium. The PET imagery was segmented into regions of interest representing glucose metabolic rates within the brain. Multi-layer perceptron neural networks were used as the base classifier for the augmented stacked generalization algorithm, creating three overall biomarker experts for AD diagnosis. The features extracted from each biomarker were used to train classifiers on various subsets of the cohort data; the decisions from these classifiers were then combined to achieve decision-based data fusion. This study found that EEG, MRI and PET data each hold complementary information for the diagnosis of AD. The use of all three in tandem provides greater diagnostic accuracy than using any single biomarker alone. The highest accuracy obtained through the EEG expert was 86.1 ±3.2%, with MRI and PET reaching 91.1 +3.2% and 91.2 ±3.9%, respectively. The maximum diagnostic accuracy of these systems averaged 95.0 ±3.1% when all three biomarkers were combined through the decision fusion algorithm described in this study. The severity analysis for AD showed similar results, with combination performance exceeding that of any biomarker expert alone

A CAD system for early diagnosis of autism using different imaging modalities.

The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome