Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.

In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.

Prefrontal dysfunction in post-COVID-19 hyposmia: an EEG/fNIRS study.

Introduction: Subtle cognitive dysfunction and mental fatigue are frequent after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, characterizing the so-called long COVID-19 syndrome. This study aimed to correlate cognitive, neurophysiological, and olfactory function in a group of subjects who experienced acute SARS-CoV-2 infection with persistent hyposmia at least 12 weeks before the observation. Methods: For each participant (32 post-COVID-19 patients and 16 controls), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data were acquired using an integrated EEG-fNIRS system during the execution of a P300 odd-ball task and a Stroop test. The Sniffin' Sticks test was conducted to assess subjects' olfactory performance. The Montreal Cognitive Assessment (MoCA) and the Frontal Assessment Battery (FAB) were also administered. Results: The post-COVID-19 group consisted of 32 individuals (20 women and 12 men) with an average education level of 12.9 ± 3.12 years, while the control group consisted of 16 individuals (10 women and 6 men) with an average education level of 14.9 ± 3.2 years. There were no significant differences in gender (X2 = 0, p = 1) or age between the two groups (age 44.81 ± 13.9 vs. 36.62 ± 11.4, p = 0.058). We identified a lower concentration of oxyhemoglobin (p < 0.05) at the prefrontal cortical level in post-COVID-19 subjects during the execution of the Stroop task, as well as a reduction in the amplitude of the P3a response. Moreover, we found that post-COVID-19 subjects performed worst at the MoCA screening test (p = 0.001), Sniffin's Sticks test (p < 0.001), and Stroop task response latency test (p < 0.001). Conclusions: This study showed that post-COVID-19 patients with persistent hyposmia present mild deficits in prefrontal function, even 4 months after the end of the infection. These deficits, although subtle, could have long-term implications for quality of life and cognitive wellbeing. It is essential to continue monitoring and evaluating these patients to better understand the extent and duration of cognitive impairments associated with long COVID-19.

An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Manual lesion segmentations for traumatic brain injury characterization.

Traumatic brain injury (TBI) often results in heterogenous lesions that can be visualized through various neuroimaging techniques, such as magnetic resonance imaging (MRI). However, injury burden varies greatly between patients and structural deformations often impact usability of available analytic algorithms. Therefore, it is difficult to segment lesions automatically and accurately in TBI cohorts. Mislabeled lesions will ultimately lead to inaccurate findings regarding imaging biomarkers. Therefore, manual segmentation is currently considered the gold standard as this produces more accurate masks than existing automated algorithms. These masks can provide important lesion phenotype data including location, volume, and intensity, among others. There has been a recent push to investigate the correlation between these characteristics and the onset of post traumatic epilepsy (PTE), a disabling consequence of TBI. One motivation of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is to identify reliable imaging biomarkers of PTE. Here, we report the protocol and importance of our manual segmentation process in patients with moderate-severe TBI enrolled in EpiBioS4Rx. Through these methods, we have generated a dataset of 127 validated lesion segmentation masks for TBI patients. These ground-truths can be used for robust PTE biomarker analyses, including optimization of multimodal MRI analysis via inclusion of lesioned tissue labels. Moreover, our protocol allows for analysis of the refinement process. Though tedious, the methods reported in this work are necessary to create reliable data for effective training of future machine-learning based lesion segmentation methods in TBI patients and subsequent PTE analyses.

Galcanezumab treatment changes visual related EEG connectivity patterns in migraine patients.

BACKGROUND: Monoclonal antibodies against calcitonin gene-related peptides (CGRP) are innovative therapies for migraine treatment. Although they are clinically effective, how anti-CGRP treatment reduces migraine attacks still remains unclear. OBJECTIVE: In this observational case-control study, we aimed to apply graph theory to EEG data from 20 migraine patients and 10 controls to investigate the effects of 3 months of galcanezumab on brain connectivity. METHODS: We analyzed EEG rhythms during black-white pattern reversal stimulation with 0.5 cycle per degree spatial frequency before (T0) galcanezumab injection, as well as after 3 months (T2). EEG recordings made 1 hour after galcanezumab administration served as the control session (T1). Patients' connectivity patterns obtained at T0, T1 and T2 were compared with normal controls. RESULTS: We found that galcanezumab increased network integration (with a 5% significance level corrected with the false discovery rate), changing the intensity of connections between the occipital through the frontal areas. At 3 months follow up, patients with persistent high headache intensity had a minor effect on the strength of connections (evaluated using Kendall's rank correlation test and p < 0.05). CONCLUSIONS: The potent anti-nociceptive action that galcanezumab exerts at a peripheral level could restore cortical connections and possibly factors predisposing to attack onset.

Functional connectivity alterations in traumatic brain injury patients with late seizures.

PTE is a neurological disorder characterized by recurrent and spontaneous epileptic seizures. PTE is a major public health problem occurring in 2-50% of TBI patients. Identifying PTE biomarkers is crucial for the development of effective treatments. Functional neuroimaging studies in patients with epilepsy and in epileptic rodents have observed that abnormal functional brain activity plays a role in the development of epilepsy. Network representations of complex systems ease quantitative analysis of heterogeneous interactions within a unified mathematical framework. In this work, graph theory was used to study resting state functional magnetic resonance imaging (rs-fMRI) and reveal functional connectivity abnormalities that are associated with seizure development in traumatic brain injury (TBI) patients. We examined rs-fMRI of 75 TBI patients from Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) which aims to identify validated Post-traumatic epilepsy (PTE) biomarkers and antiepileptogenic therapies using multimodal and longitudinal data acquired from 14 international sites. The dataset includes 28 subjects who had at least one late seizure after TBI and 47 subjects who had no seizures within 2 years post-injury. Each subject's neural functional network was investigated by computing the correlation between the low frequency time series of 116 regions of interest (ROIs). Each subject's functional organization was represented as a network consisting of nodes, brain regions, and edges that show the relationship between the nodes. Then, several graph measures concerning the integration and the segregation of the functional brain networks were extracted in order to highlight changes in functional connectivity between the two TBI groups. Results showed that the late seizure-affected group had a compromised balance between integration and segregation and presents functional networks that are hyperconnected, hyperintegrated but at the same time hyposegregated compared with seizure-free patients. Moreover, TBI subjects who developed late seizures had more low betweenness hubs.

Effect of Single Session of Anodal M1 Transcranial Direct Current Stimulation-TDCS-On Cortical Hemodynamic Activity: A Pilot Study in Fibromyalgia.

Transcranial direct current stimulation (TDCS) on the primary motor cortex (M1) has been reported to be effective in fibromyalgia (FM). Our previous works have shown hypometabolism of motor networks in FM using Functional Near Infrared Spectroscopy (fNIRS), which could contribute to pain symptoms. To investigate if a single Transcranial Direct Current Stimulation (TDCS) session can restore the reduced metabolism expected in FM patients, we compared metabolic activity in FM patients and controls during a finger-tapping task in basal condition, sham condition, and under anodal TDCS on M1. During the finger tapping task, a continuous wave 20 channel fNIRS system was placed across the bilateral central-frontal areas in 22 healthy controls and 54 FM patients. Subjects were randomly assigned to real TDCS or sham stimulation. The finger-tapping slowness did not change after real and sham stimulation. After real TDCS stimulation, FM patients showed an increased activation of cortical motor regions (t-statistic = -2.5246, p-value = 0.0125 for the stimulated hemisphere and t-statistic = -4.6638, p-value = 0.0001 for the non-stimulated hemisphere). The basal differences between FM and controls reverted after real TDCS, while this effect was not observed for sham stimulation. A single TDCS session of the cortical motor network seemed able to restore basic cortical hypometabolism in FM patients. Further studies could clarify the long-term effect of M1 stimulation on cortical metabolism, and its relevance in pain processing and clinical features.

Effects of a 12-Week Periodized Resistance Training Program on Resting Brain Activity and Cerebrovascular Function: A Nonrandomized Pilot Trial.

Resistance training is a promising strategy to promote healthy cognitive aging; however, the brain mechanisms by which resistance training benefits cognition have yet to be determined. Here, we examined the effects of a 12-week resistance training program on resting brain activity and cerebrovascular function in 20 healthy older adults (14 females, mean age 69.1 years). In this single group clinical trial, multimodal 3 T magnetic resonance imaging was performed at 3 time points: baseline (preceding a 12-week control period), pre-intervention, and post-intervention. Along with significant improvements in fluid cognition (d = 1.27), 4 significant voxelwise clusters were identified for decreases in resting brain activity after the intervention (Cerebellum, Right Middle Temporal Gyrus, Left Inferior Parietal Lobule, and Right Inferior Parietal Lobule), but none were identified for changes in resting cerebral blood flow. Using a separate region of interest approach, we provide estimates for improved cerebral blood flow, compared with declines over the initial control period, in regions associated with cognitive impairment, such as hippocampal blood flow (d = 0.40), and posterior cingulate blood flow (d = 0.61). Finally, resistance training had a small countermeasure effect on the age-related progression of white matter lesion volume (rank-biserial = -0.22), a biomarker of cerebrovascular disease. These proof-of-concept data support larger trials to determine whether resistance training can attenuate or even reverse salient neurodegenerative processes.

Central effects of galcanezumab in migraine: a pilot study on Steady State Visual Evoked Potentials and occipital hemodynamic response in migraine patients.

BACKGROUND: The discovery of the prominent action of Calcitonin Gene Related Peptide -CGRP- on trigeminal afferents and meningeal vessels, opened a new era in migraine treatment. However, how the block of nociceptive afferents could act on central mechanisms of migraine is still not clear. In this pilot study we aimed to test the effect of 3 months Galcanezumab (CGA) therapy on occipital visual reactivity in migraine patients, using the Steady State Visual Evoked Potentials-SSVEPs and Functional Near Infrared Spectroscopy -fNIRS. METHOD: Thirteen migraine patients underwent clinical and neurophysiological examination in basal condition (T0), 1 h after GCA injection (T1) and after 3 months of GCA treatment (T2). Ten healthy volunteers were also evaluated. RESULTS: At T2, there was a reduction of headache frequency and disability. At T2, the EEG power significantly diminished as compared to T0 and T1 at occipital sites, and the topographical analysis confirmed a restoration of SSVEPs within normal values. The Oxyhemoglobin levels in occipital cortex, which were basically increased during visual stimulation in migraine patients, reverted to normal values at T2. CONCLUSIONS: The present pilot study indicates that Galcanezumab could act on cortical targets located beyond the pain network, restoring the abnormal occipital reactivity. This effect could indicate the possible disease modifying properties of CGRP related monoclonal antibodies.

Post-lockdown infection rates of COVID-19 following the reopening of public businesses.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic warranted a myriad of government-ordered business closures across the USA in efforts to mitigate the spread of the virus. This study aims to discover the implications of government-enforced health policies of reopening public businesses amidst the pandemic and its effect on county-level infection rates. METHODS: Eighty-three US counties (n = 83) that reported at least 20 000 cases as of 4 November 2020 were selected for this study. The dates when businesses (restaurants, bars, retail, gyms, salons/barbers and public schools) partially and fully reopened, as well as infection rates on the 1st and 14th days following each businesses' reopening, were recorded. Regression analysis was conducted to deduce potential associations between the 14-day change in infection rate and mask usage frequency, median household income, population density and social distancing. RESULTS: On average, infection rates rose significantly as businesses reopened. The average 14-day change in infection rate was higher for fully reopened businesses (infection rate = +0.100) compared to partially reopened businesses (infection rate = +0.0454). The P-value of the two distributions was 0.001692, indicating statistical significance (P < 0.01). CONCLUSION: This research provides insight into the transmission of COVID-19 and promotes evidence-driven policymaking for disease prevention and community health.

Association between ABO blood types and coronavirus disease 2019 (COVID-19), genetic associations, and underlying molecular mechanisms: a literature review of 23 studies.

An association of various blood types and the 2019 novel coronavirus disease (COVID-19) has been found in a number of publications. The aim of this literature review is to summarize key findings related to ABO blood types and COVID-19 infection rate, symptom presentation, and outcome. Summarized findings include associations between ABO blood type and higher infection susceptibility, intubation duration, and severe outcomes, including death. The literature suggests that blood type O may serve as a protective factor, as individuals with blood type O are found COVID-19 positive at far lower rates. This could suggest that blood type O individuals are less susceptible to infection, or that they are asymptomatic at higher rates and therefore do not seek out testing. We also discuss genetic associations and potential molecular mechanisms that drive the relationship between blood type and COVID-19. Studies have found a strong association between a locus on a specific gene cluster on chromosome three (chr3p21.31) and outcome severity, such as respiratory failure. Cellular models have suggested an explanation for blood type modulation of infection, evidencing that spike protein/Angiotensin-converting enzyme 2 (ACE2)-dependent adhesion to ACE2-expressing cell lines was specifically inhibited by monoclonal or natural human anti-A antibodies, so individuals with non-A blood types, specifically O, or B blood types, which produce anti-A antibodies, may be less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to the inhibitory effects of anti-A antibodies.

Distribution and volume analysis of early hemorrhagic contusions by MRI after traumatic brain injury: a preliminary report of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx).

Traumatic brain injury (TBI) can produce heterogeneous injury patterns including a variety of hemorrhagic and non-hemorrhagic lesions. The impact of lesion size, location, and interaction between total number and location of contusions may influence the occurrence of seizures after TBI. We report our methodologic approach to this question in this preliminary report of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We describe lesion identification and segmentation of hemorrhagic contusions by early posttraumatic magnetic resonance imaging (MRI). We describe the preliminary methods of manual lesion segmentation in an initial cohort of 32 TBI patients from the EpiBioS4Rx cohort and the preliminary association of hemorrhagic contusion and edema location and volume to seizure incidence.

Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI.

Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.

Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients.

Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE.

Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study.

BACKGROUND: Traumatic brain injury (TBI) causes early seizures and is the leading cause of post-traumatic epilepsy. We prospectively assessed structural imaging biomarkers differentiating patients who develop seizures secondary to TBI from patients who do not. DESIGN: Multicentre prospective cohort study starting in 2018. Imaging data are acquired around day 14 post-injury, detection of seizure events occurred early (within 1 week) and late (up to 90 days post-TBI). RESULTS: From a sample of 96 patients surviving moderate-to-severe TBI, we performed shape analysis of local volume deficits in subcortical areas (analysable sample: 57 patients; 35 no seizure, 14 early, 8 late) and cortical ribbon thinning (analysable sample: 46 patients; 29 no seizure, 10 early, 7 late). Right hippocampal volume deficit and inferior temporal cortex thinning demonstrated a significant effect across groups. Additionally, the degree of left frontal and temporal pole thinning, and clinical score at the time of the MRI, could differentiate patients experiencing early seizures from patients not experiencing them with 89% accuracy. CONCLUSIONS AND RELEVANCE: Although this is an initial report, these data show that specific areas of localised volume deficit, as visible on routine imaging data, are associated with the emergence of seizures after TBI.

Imaging biomarkers of posttraumatic epileptogenesis.

Traumatic brain injury (TBI) affects 2.5 million people annually within the United States alone, with over 300 000 severe injuries resulting in emergency room visits and hospital admissions. Severe TBI can result in long-term disability. Posttraumatic epilepsy (PTE) is one of the most debilitating consequences of TBI, with an estimated incidence that ranges from 2% to 50% based on severity of injury. Conducting studies of PTE poses many challenges, because many subjects with TBI never develop epilepsy, and it can be more than 10 years after TBI before seizures begin. One of the unmet needs in the study of PTE is an accurate biomarker of epileptogenesis, or a panel of biomarkers, which could provide early insights into which TBI patients are most susceptible to PTE, providing an opportunity for prophylactic anticonvulsant therapy and enabling more efficient large-scale PTE studies. Several recent reviews have provided a comprehensive overview of this subject (Neurobiol Dis, 123, 2019, 3; Neurotherapeutics, 11, 2014, 231). In this review, we describe acute and chronic imaging methods that detect biomarkers for PTE and potential mechanisms of epileptogenesis. We also describe shortcomings in current acquisition methods, analysis, and interpretation that limit ongoing investigations that may be mitigated with advancements in imaging techniques and analysis.

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age.

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.

A MACHINE LEARNING MODEL TO PREDICT SEIZURE SUSCEPTIBILITY FROM RESTING-STATE FMRI CONNECTIVITY.

Traumatic brain injury (TBI) is a leading cause of disability globally. Many patients develop post-traumatic epilepsy, or recurrent seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis that may assist in preventing seizure occurrence by identifying high-risk patients. We present a novel method of assessing seizure susceptibility using data from 49 patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We employ a machine learning paradigm that utilizes a Random Forest classifier trained with resting-state functional magnetic resonance imaging (fMRI) data to predict seizure outcomes. Following 100 rounds of stratified cross-validation with 70% of resting state fMRI scans as the training set and 30% as the testing set, our model was found to assess seizure outcome in the testing set with 69% accuracy. To validate the method, we compared our results with classification by Support Vector Machines and Neural Network classifiers.

Multiplex Networks for Early Diagnosis of Alzheimer's Disease.

Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.