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Analyzing functional brain activity through functional magnetic resonance imaging (fMRI) is commonly done using tools from graph theory for the analysis of the correlation matrices. A drawback of these methods is that the networks must be restricted to values of the weights of the edges within certain thresholds and there is no consensus about the best choice of such thresholds. Topological data analysis (TDA) is a recently-developed tool in algebraic topology which allows us to analyze networks through combinatorial spaces obtained from them, with the advantage that all the possible thresholds can be considered at once. In this paper we applied TDA, in particular persistent homology, to study correlation matrices from rs-fMRI, and through statistical analysis, we detected significant differences between the topological structures of adolescents with inhaled substance abuse disorder (ISAD) and healthy controls. We interpreted the topological differences as indicative of a loss of robustness in the functional brain networks of the ISAD population.

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BACKGROUND: Alzheimer disease (AD) leads to cognitive decline and alters functional connectivity (FC) in key brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) assesses these changes using static-FC for overall correlation and dynamic-FC for temporal variability. OBJECTIVE: In AD, there is altered FC compared to normal conditions. The present study investigates possible region-specific functional abnormalities occurring longitudinally over 1 year. Our aim is to evaluate the potential usefulness of the static and dynamic approaches in identifying biomarkers of AD progression. METHODS: The study involved 15 AD and 20 healthy participants from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) database, tracked over 2 visits within 1 year. Using constrained-independent component analysis, we assessed FC changes across 80-regions of interest in AD over the year, examining both static and dynamic conditions. RESULTS: The average regional FC decreased in AD compared to healthy subjects at baseline and after 1 year. The dynamic condition identifies similarities with a few additional changes in the FC compared to the static condition. In both analyses, the baseline assessment revealed reduced connectivity between the following regions: right-middle-occipital and left-superior-occipital, left-hippocampus and right-postcentral, left-lingual and left-fusiform, and precuneus and left-thalamus. Additionally, increased connectivity was found between the left-superior-occipital and precuneus regions. In the 1-year AD assessment, increased connectivity was noted between the right-superior-temporal-pole and right-insular, right-hippocampus and left-caudate, right-middle-occipital and right-superior-temporal-pole, and posterior-cingulate-cortex and middle-temporal-pole regions. CONCLUSION: Significant changes were observed at baseline in the frontal, occipital, and core basal-ganglia regions, progressing towards the temporal lobe and subcortical regions in the following year. After 1 year, we observed the aforementioned region-specific neurological differences in AD, significantly aiding diagnosis and disease tracking.

ANTECEDENTES: A doença de Alzheimer (DA) leva ao declínio cognitivo e altera a conectividade funcional (CF) em regiões-chave do cérebro. A ressonância magnética funcional em estado de repouso (rs-fMRI) avalia essas alterações usando CF estática para correlação geral e CF dinâmica para variabilidade temporal. OBJETIVO: Na DA, há CF alterada em relação às condições normais. O presente estudo investiga possíveis anormalidades funcionais específicas da região que ocorrem longitudinalmente ao longo de um ano. Nosso objetivo é avaliar a utilidade potencial das abordagens estáticas e dinâmicas na identificação de biomarcadores da progressão da DA. MéTODOS: O estudo envolveu 15 participantes com DA e 20 participantes saudáveis do banco de dados da Iniciativa de Neuroimagem da Doença de Alzheimer 2 (ADNI2), rastreados em duas visitas no período de um ano. Usando análise de componentes independentes e restritos, avaliamos as mudanças de CF em 80 regiões de interesse na DA ao longo do ano, examinando condições estáticas e dinâmicas. RESULTADOS: A CF regional média diminuiu na DA em comparação com indivíduos saudáveis no início do estudo e após um ano. A condição dinâmica identifica semelhanças com algumas alterações adicionais na CF em comparação com a condição estática. Em ambas as análises, a avaliação inicial revelou conectividade reduzida entre as seguintes regiões: occipital médio direito e occipital superior esquerdo, hipocampo esquerdo e pós-central direito, lingual esquerdo e fusiforme esquerdo, e precuneus e tálamo esquerdo. Além disso, foi encontrada maior conectividade entre as regiões occipital superior esquerda e precuneus. Na avaliação de DA de um ano, foi observada conectividade aumentada entre o polo temporal superior direito e o insular direito, o hipocampo direito e o caudado esquerdo, occipital médio direito e o polo temporal superior direito, e regiões posteriores do córtex cingulado e do polo temporal médio. CONCLUSãO: Mudanças significativas foram observadas no início do estudo nas regiões frontal, occipital e dos gânglios basais centrais, progredindo em direção ao lobo temporal e regiões subcorticais no ano seguinte. Após um ano, observamos as diferenças neurológicas específicas da região acima mencionadas na DA, auxiliando significativamente no diagnóstico e no rastreamento da doença.

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Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.

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The frontal aslant tract (FAT) connects the supplementary motor area (SMA) with the pars opercularis. Its role in language and its implications in glioma surgery remain under discussion. We present an anatomosurgical study of three cases with surgical resolution. Three patients with gliomas in the left frontal lobe were operated on using an awake patient protocol with cortical and subcortical mapping techniques, conducting motor and language evaluations. Tractography was performed using DSI Studio software. All three patients showed intraoperative language inhibition through subcortical stimulation of the FAT. Resection involving the FAT correlated with language deficits in all cases and movement initiation deficits in two cases. All patients recovered from their deficits at six months postoperatively. In conclusion, the tract has been successfully reconstructed, showing both anatomical and functional complexity, supporting the idea of its mapping and preservation in glioma surgery. Future interdisciplinary studies are necessary to determine the transient or permanent nature of the deficits.

El tracto oblicuo frontal (TOF) conecta el área motora suplementaria (AMS) con la pars opercularis. Su rol en el lenguaje y su implicancia en la cirugía de gliomas siguen en discusión. Presentamos un estudio anatomoquirúrgico de tres casos con resolución quirúrgica. Se operaron tres pacientes con gliomas en el lóbulo frontal izquierdo utilizando protocolo de paciente despierto con técnicas de mapeo cortical y subcortical realizando evaluación motora y del lenguaje. Las tractografías fueron realizadas con el software DSI Studio. Los tres pacientes presentaron inhibición intraoperatoria del lenguaje mediante la estimulación subcortical de TOF. La resección en contacto con el TOF se correlacionó con déficits del lenguaje en todos los casos y en dos casos déficits en la iniciación del movimiento. Todos los pacientes recuperaron su déficit a los seis meses postoperatorios. En conclusión, se ha logrado reconstruir al tracto. Éste presenta una complejidad anatómica y funcional, que apoya la idea de su mapeo y preservación en la cirugía de gliomas. Futuros estudios interdisciplinarios son necesarios para determinar el carácter transitorio o permanente de los déficits.

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Semantic verbal fluency (SVF) impairment is present in several neurological disorders. Although activation in SVF-related areas has been reported, how these regions are connected and their functional roles in the network remain divergent. We assessed SVF static and dynamic functional connectivity (FC) and effective connectivity in healthy participants using functional magnetic resonance imaging. We observed activation in the inferior frontal (IFG), middle temporal (pMTG) and angular gyri (AG), anterior cingulate (AC), insular cortex, and regions of the superior, middle, and medial frontal gyri (SFG, MFG, MidFG). Our static FC analysis showed a highly interconnected task and resting state network. Increased connectivity of AC with the pMTG and AG was observed for the task. The dynamic FC analysis provided circuits with connections similarly modulated across time and regions related to category identification, language comprehension, word selection and recovery, word generation, inhibition of speaking, speech planning, and articulatory planning of orofacial movements. Finally, the effective connectivity analysis provided a network that best explained our data, starting at the AG and going to the pMTG, from which there was a division between the ventral and dorsal streams. The SFG and MFG regions were connected and modulated by the MidFG, while the inferior regions formed the ventral stream. Therefore, we successfully assessed the SVF network, exploring regions associated with the entire processing, from category identification to word generation. The methodological approach can be helpful for further investigation of the SVF network in neurological disorders.

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Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This study optimized methodologies to explore differences in ictal and interictal spatiotemporal activation patterns based on visual stimuli using fMRI in two patients with unique aura triggers. Both patients underwent separate fMRI sessions during the ictal and interictal periods. The Gaussian Process Classifier (GPC) was used to differentiate these periods by employing a machine learning temporal embedding approach and spatiotemporal activation patterns based on visual stimuli. When restricted to visual and occipital regions, GPC had an improved performance, with accuracy rates for patients A and B of roughly 86-90% and 77-81%, respectively (p < 0.01). The algorithm effectively differentiated visual stimulation and rest periods and identified times when aura symptoms manifested, as evident from the varying predicted probabilities in the GPC models. These findings contribute to our understanding of the role of visual processing and brain activity patterns in migraine with aura and the significance of temporal embedding techniques in examining aura phenomena. This finding has implications for diagnostic tools and therapeutic techniques, especially for patients suffering from aura symptoms.

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When engaged in a conversation, one receives auditory information from the other's speech but also from their own speech. However, this information is processed differently by an effect called Speech-Induced Suppression. Here, we studied brain representation of acoustic properties of speech in natural unscripted dialogues, using electroencephalography (EEG) and high-quality speech recordings from both participants. Using encoding techniques, we were able to reproduce a broad range of previous findings on listening to another's speech, and achieving even better performances when predicting EEG signal in this complex scenario. Furthermore, we found no response when listening to oneself, using different acoustic features (spectrogram, envelope, etc.) and frequency bands, evidencing a strong effect of SIS. The present work shows that this mechanism is present, and even stronger, during natural dialogues. Moreover, the methodology presented here opens the possibility of a deeper understanding of the related mechanisms in a wider range of contexts.

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Despite great advancements and the diffusion of awake surgery for brain tumors, the literature shows that the tests applied during the procedure are heterogeneous and non-standardized. This prospective, observational, descriptive study collected data on intraoperative brain mapping and the performance of multiple neurocognitive tests in 51 awake surgeries for diffuse low-grade glioma. Frequency of use and rate of intraoperative findings of different neurocognitive tests were analyzed. Patients mean age at the time of surgery was 35.1 (20-57) years. We performed 26 (51.0%) surgeries on the left hemisphere (LH) and 25 (49.0%) on the right hemisphere (RH). Significant differences were observed between the total number of functional findings (cortical and subcortical) identified in the LH and RH (p = 0.004). In subcortical findings alone, the differences remained significant (p = 0.0004). The RH subcortical region showed the lowest number of intraoperative findings, and this was correlated with functional outcome: Karnofsky performance scale at five days (p = 0.022), three months (p = 0.002) and one year (p = 0.002) post-surgery. On average, more tests were used to map the RH, with a lower frequency of both cortical and subcortical functional findings. Even though subcortical findings were less frequent than cortical findings, they were crucial to defining the resection margins. Based on the intraoperative findings, frequency of use, and rate of findings per use of the tests analyzed, the most relevant tests for each hemisphere for awake brain mapping were identified.

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Few resting-state functional magnetic resonance imaging (RS-fMRI) studies evaluated the impact of acute ischemic changes on cerebral functional connectivity (FC) and its relationship with functional outcomes after acute ischemic stroke (AIS), considering the side of lesions. To characterize alterations of FC of patients with AIS by analyzing 12 large-scale brain networks (NWs) with RS-fMRI. Additionally, we evaluated the impact of the side (right (RH) or left (LH) hemisphere) of insult on the disruption of brain NWs. 38 patients diagnosed with AIS (17 RH and 21 LH) who performed 3T MRI scans up to 72 h after stroke were compared to 44 healthy controls. Images were processed and analyzed with the software toolbox UF2C with SPM12. For the first level, we generated individual matrices based on the time series extraction from 70 regions of interest (ROIs) from 12 functional NWs, constructing Pearson's cross-correlation; the second-level analysis included an analysis of covariance (ANCOVA) to investigate differences between groups. The statistical significance was determined with p < 0.05, after correction for multiple comparisons with false discovery rate (FDR) correction. Overall, individuals with LH insults developed poorer clinical outcomes after six months. A widespread pattern of lower FC was observed in the presence of LH insults, while a contralateral pattern of increased FC was identified in the group with RH insults. Our findings suggest that LH stroke causes a severe and widespread pattern of reduction of brain networks' FC, presumably related to the impairment in their long-term recovery.

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Humans often face the challenge of making decisions between ambiguous options. The level of ambiguity in decision-making has been linked to activity in the parietal cortex, but its exact computational role remains elusive. To test the hypothesis that the parietal cortex plays a causal role in computing ambiguous probabilities, we conducted consecutive fMRI and TMS-EEG studies. We found that participants assigned unknown probabilities to objective probabilities, elevating the uncertainty of their decisions. Parietal cortex activity correlated with the objective degree of ambiguity and with a process that underestimates the uncertainty during decision-making. Conversely, the midcingulate cortex (MCC) encodes prediction errors and increases its connectivity with the parietal cortex during outcome processing. Disruption of the parietal activity increased the uncertainty evaluation of the options, decreasing cingulate cortex oscillations during outcome evaluation and lateral frontal oscillations related to value ambiguous probability. These results provide evidence for a causal role of the parietal cortex in computing uncertainty during ambiguous decisions made by humans.

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OBJECTIVE: During hypnosis, significant changes in the BOLD signal associated with the anterior default mode network (DMN) and prefrontal attentional systems have been reported as evidence of dissociation defined since Charcot. However, it remains uncertain whether these changes are mainly attributable to the hypnotic state per se or to the target suggestions used to verify subject's state during neuroimaging studies. The aim of the present study is to evidence the brain in hypnosis, contrasting the common resting state versus neutral hypnosis (hypnosis in the absence of target suggestions). METHODS: Twenty-four healthy right-handed volunteers (age 28.3 y.o., 12 females) rated moderate hypnotic responsiveness underwent resting state fMRI at 3.0 T in two sessions, once in neutral hypnosis and the other in the common resting state. Each subject's functional data were analyzed for low-frequency BOLD signal correlations seed-to-voxel for the whole brain in the first-level analysis, and seed-to-voxel in a second-level analysis to estimate group results using seeds for five resting state networks: the default mode (DMN), the central executive (CEN), the salience (SaN), the dorso-lateral attention (DAN), and the sensorimotor (SMN) networks. RESULTS: In general, all network maps of the hypnotic condition presented higher connectivity than those of the resting condition. However, only contrasts for the DAN, SaN, and SMN were statistically significant, including correlated out-of-the-network regions. CONCLUSION: Parietal and occipital regions displayed increased connectivity across networks, implying dissociation from the frontal cortices. This is the first fMRI intrinsic study of hypnosis without target suggestion.

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All experiences preserved within episodic memory contain information on the space and time of events. The hippocampus is the main brain region involved in processing spatial and temporal information for incorporation within episodic memory representations. However, the other brain regions involved in the encoding and retrieval of spatial and temporal information within episodic memory are unclear, because a systematic review of related studies is lacking and the findings are scattered. The present study was designed to integrate the results of functional magnetic resonance imaging and positron emission tomography studies by means of a systematic review and meta-analysis to provide converging evidence. In particular, we focused on identifying the brain regions involved in the retrieval of spatial and temporal information. We identified a spatial retrieval network consisting of the inferior temporal gyrus, parahippocampal gyrus, superior parietal lobule, angular gyrus, and precuneus. Temporal context retrieval was supported by the dorsolateral prefrontal cortex. Thus, the retrieval of spatial and temporal information is supported by different brain regions, highlighting their different natures within episodic memory.

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OBJECTIVE: Patients with multifocal or generalized epilepsies manifesting with drop attacks have severe refractory seizures and significant cognitive and behavioural abnormalities. It is unclear to what extent these features relate to network abnormalities and how networks in sensorimotor cortex differ from those in patients with refractory focal epilepsies. Thus, in this study we sought to provide preliminary data on connectivity of sensorimotor cortex in patients with epileptic drop attacks, in comparison to patients with focal refractory epilepsies. METHODS: Resting-state fMRI (rs-fMRI) data was available for 5 patients with epileptic drop attacks and 15 with refractory focal epilepsies undergoing presurgical evaluation. Functional connectivity was analyzed with a seed-based protocol, with primary seeds placed at the precentral gyrus, the postcentral gyrus and the premotor cortex. For each seed, the subjects' timeseries were extracted and transformed to Z scores. Between-group analysis was then performed using the 3dttest+ + AFNI program. RESULTS: Two clusters of reduced connectivity in the group with drop attacks (DA group) in relation to those with focal epilepsies were found in the between-group analysis: the precentral seed showed reduced connectivity in the surrounding motor area, and the postcentral seed, reduced connectivity with the ipsilateral posterior cingulate gyrus. In the intra-group analyses, sensorimotor and premotor networks were abnormal in the DA group, whereas patients with focal epilepsies had the usual connectivity maps with each seed. CONCLUSION: This pilot study shows differences in the cerebral connectivity in the sensorimotor cortex of patients with generalized epilepsies and drop attacks which should be further explored to better understand the biological bases of the seizure generation and cognitive changes in these people.

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Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.

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Memory retrieval does not provide a perfect recapitulation of past events, but instead an imperfect reconstruction of event-specific details and general knowledge. However, it remains unclear whether this reconstruction relies on mixtures of signals from different memory systems, including one supporting general knowledge. Here, we investigate whether the anterior temporal lobe (ATL) distorts new memories because of prior category knowledge. In this preregistered experiment (n = 36), participants encoded and retrieved image-location associations. Most images' locations were clustered according to their category, but some were in random locations. With this protocol, we previously demonstrated that randomly located images were retrieved closer to their category cluster relative to their encoded locations, suggesting an influence of category knowledge. We combined this procedure with TMS delivered to the left ATL before retrieval. We separately examined event-specific details (error) and category knowledge (bias) to identify distinct signals attributable to different memory systems. We found that TMS to ATL attenuated bias in location memory, but this effect was limited to exploratory analyses of atypical category members of animal categories. The magnitude of error was not impacted, suggesting that a memory's fidelity can be decoupled from its distortion by category knowledge. This raises the intriguing possibility that retrieval is jointly supported by separable memory systems.

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Sound synthesis refers to the creation of original acoustic signals with broad applications in artistic innovation, such as music creation for games and videos. Nonetheless, machine learning architectures face numerous challenges when learning musical structures from arbitrary corpora. This issue involves adapting patterns borrowed from other contexts to a concrete composition objective. Using Labeled Correlation Alignment (LCA), we propose an approach to sonify neural responses to affective music-listening data, identifying the brain features that are most congruent with the simultaneously extracted auditory features. For dealing with inter/intra-subject variability, a combination of Phase Locking Value and Gaussian Functional Connectivity is employed. The proposed two-step LCA approach embraces a separate coupling stage of input features to a set of emotion label sets using Centered Kernel Alignment. This step is followed by canonical correlation analysis to select multimodal representations with higher relationships. LCA enables physiological explanation by adding a backward transformation to estimate the matching contribution of each extracted brain neural feature set. Correlation estimates and partition quality represent performance measures. The evaluation uses a Vector Quantized Variational AutoEncoder to create an acoustic envelope from the tested Affective Music-Listening database. Validation results demonstrate the ability of the developed LCA approach to generate low-level music based on neural activity elicited by emotions while maintaining the ability to distinguish between the acoustic outputs.

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Navigated transcranial magnetic stimulation (nTMS) is a valuable tool for non-invasive brain stimulation. Currently, nTMS requires fixing of markers on the patient's head. Head marker displacements lead to changes in coil placement and brain stimulation inaccuracy. A markerless neuronavigation method is needed to increase the reliability of nTMS and simplify the nTMS protocol. In this study, we introduce and release MarLe, a Python markerless head tracker neuronavigation software for TMS. This novel software uses computer-vision techniques combined with low-cost cameras to estimate the head pose for neuronavigation. A coregistration algorithm, based on a closed-form solution, was designed to track the patient's head and the TMS coil referenced to the individual's brain image. We show that MarLe can estimate head pose based on real-time video processing. An intuitive pipeline was developed to connect the MarLe and nTMS neuronavigation software. MarLe achieved acceptable accuracy and stability in a mockup nTMS experiment. MarLe allows real-time tracking of the patient's head without any markers. The combination of face detection and a coregistration algorithm can overcome nTMS head marker displacement concerns. MarLe can improve reliability, simplify, and reduce the protocol time of brain intervention techniques such as nTMS.

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Autism is a multifaceted neurodevelopmental condition whose accurate diagnosis may be challenging because the associated symptoms and severity vary considerably. The wrong diagnosis can affect families and the educational system, raising the risk of depression, eating disorders, and self-harm. Recently, many works have proposed new methods for the diagnosis of autism based on machine learning and brain data. However, these works focus on only one pairwise statistical metric, ignoring the brain network organization. In this paper, we propose a method for the automatic diagnosis of autism based on functional brain imaging data recorded from 500 subjects, where 242 present autism spectrum disorder considering the regions of interest throughout Bootstrap Analysis of Stable Cluster map. Our method can distinguish the control group from autism spectrum disorder patients with high accuracy. Indeed the best performance provides an AUC near 1.0, which is higher than that found in the literature. We verify that the left ventral posterior cingulate cortex region is less connected to an area in the cerebellum of patients with this neurodevelopment disorder, which agrees with previous studies. The functional brain networks of autism spectrum disorder patients show more segregation, less distribution of information across the network, and less connectivity compared to the control cases. Our workflow provides medical interpretability and can be used on other fMRI and EEG data, including small data sets.

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BACKGROUND: Psychosis is related to neurochemical changes in deep-brain nuclei, particularly suggesting dopamine dysfunctions. We used an magnetic resonance imaging-based technique called quantitative susceptibility mapping (QSM) to study these regions in psychosis. QSM quantifies magnetic susceptibility in the brain, which is associated with iron concentrations. Since iron is a cofactor in dopamine pathways and co-localizes with inhibitory neurons, differences in QSM could reflect changes in these processes. METHODS: We scanned 83 patients with first-episode psychosis and 64 healthy subjects. We reassessed 22 patients and 21 control subjects after 3 months. Mean susceptibility was measured in 6 deep-brain nuclei. Using linear mixed models, we analyzed the effect of case-control differences, region, age, gender, volume, framewise displacement (FD), treatment duration, dose, laterality, session, and psychotic symptoms on QSM. RESULTS: Patients showed a significant susceptibility reduction in the putamen and globus pallidus externa (GPe). Patients also showed a significant R2* reduction in GPe. Age, gender, FD, session, group, and region are significant predictor variables for QSM. Dose, treatment duration, and volume were not predictor variables of QSM. CONCLUSIONS: Reduction in QSM and R2* suggests a decreased iron concentration in the GPe of patients. Susceptibility reduction in putamen cannot be associated with iron changes. Since changes observed in putamen and GPe were not associated with symptoms, dose, and treatment duration, we hypothesize that susceptibility may be a trait marker rather than a state marker, but this must be verified with long-term studies.

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PURPOSE: QSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms. METHODS: Synthetic susceptibility and R 2 * $$ {R}_2^{\ast } $$ maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility and R 2 * $$ {R}_2^{\ast } $$ values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM and R 2 * $$ {R}_2^{\ast } $$ maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms. RESULTS: QSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in-phase acquisitions, water-fat separation methods, and QSM dipole inversion algorithms. CONCLUSION: The phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility, R 2 * $$ {R}_2^{\ast } $$ and TEs, and thus creates different abdominal scenarios.

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