A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease.
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ABSTRACT:
SUBMITTER: Tait L
PROVIDER: S-EPMC8382184 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patient's virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.
| S-EPMC6405161 | biostudies-literature
Project description:BackgroundNeuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer's disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients.MethodsTo simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline.ResultsAll but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4-8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD.ConclusionsUsing a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD.
| S-EPMC9310500 | biostudies-literature
Project description:Huntington's disease (HD) is a progressive neurodegenerative disorder that can be diagnosed with certainty decades before symptom onset. Studies using structural MRI have identified grey matter (GM) loss predominantly in the striatum, but also involving various cortical areas. So far, voxel-based morphometric studies have examined each brain region in isolation and are thus unable to assess the changes in the interrelation of brain regions. Here, we examined the structural covariance in GM volumes in pre-specified motor, working memory, cognitive flexibility, and social-affective networks in 99 patients with manifest HD (mHD), 106 presymptomatic gene mutation carriers (pre-HD), and 108 healthy controls (HC). After correction for global differences in brain volume, we found that increased GM volume in one region was associated with increased GM volume in another. When statistically comparing the groups, no differences between HC and pre-HD were observed, but increased positive correlations were evident for mHD, relative to pre-HD and HC. These findings could be explained by a HD-related neuronal loss heterogeneously affecting the examined network at the pre-HD stage, which starts to dominate structural covariance globally at the manifest stage. Follow-up analyses identified structural connections between frontoparietal motor regions to be linearly modified by disease burden score (DBS). Moderator effects of disease load burden became significant at a DBS level typically associated with the onset of unequivocal HD motor signs. Together with existing findings from functional connectivity analyses, our data indicates a critical role of these frontoparietal regions for the onset of HD motor signs.
| S-EPMC6867397 | biostudies-literature
Project description:BackgroundAlthough lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer's disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear.MethodsWe combined large-scale brain resting-state networks (RSNs) approaches with canonical correlation analysis to explore the accumulating effects of lipid gene- and protein-centric levels on cerebrospinal fluid (CSF) biomarkers, dynamic trajectory of large-scale RSNs, and cognitive performance across entire AD spectrum. Support vector machine model was used to distinguish AD spectrum and pathway analysis was used to test the influences among these variables.ResultsWe found that the effects of accumulation of lipid-pathway genetic variants and lipoproteins were significantly correlated with CSF biomarkers levels and cognitive performance across the AD spectrum. Dynamic trajectory of large-scale RSNs represented a rebounding mode, which is characterized by a weakened network cohesive connector role and enhanced network incohesive provincial role following disease progression. Importantly, the fluctuating large-scale RSNs connectivity was significantly correlated with the summative effects of lipid-pathway genetic variants and lipoproteins, CSF biomarkers, and cognitive performance. Moreover, SVM model revealed that the lipid-associated twenty-two brain network connections represented higher capacity to classify AD spectrum. Pathway analysis further identified dyslipidemia directly influenced brain network reorganization or indirectly affected the CSF biomarkers and subsequently caused cognitive decline.ConclusionsDyslipidemia exacerbated cognitive decline and increased the risk of AD via mediating large-scale brain networks integrity and promoting neuropathological processes. These findings reveal a role for lipid metabolism in AD pathogenesis and suggest lipid management as a potential therapeutic target for AD.
| S-EPMC9724298 | biostudies-literature
Project description:Autism is a complex neurological condition characterized by childhood onset of dysfunction in multiple cognitive domains including socio-emotional function, speech and language, and processing of internally versus externally directed stimuli. Although gross brain anatomic differences in autism are well established, recent studies investigating regional differences in brain structure and function have yielded divergent and seemingly contradictory results. How regional abnormalities relate to the autistic phenotype remains unclear. We hypothesized that autism exhibits distinct perturbations in network-level brain architecture, and that cognitive dysfunction may be reflected by abnormal network structure. Network-level anatomic abnormalities in autism have not been previously described. We used structural covariance MRI to investigate network-level differences in gray matter structure within two large-scale networks strongly implicated in autism, the salience network and the default mode network, in autistic subjects and age-, gender-, and IQ-matched controls. We report specific perturbations in brain network architecture in the salience and default-mode networks consistent with clinical manifestations of autism. Extent and distribution of the salience network, involved in social-emotional regulation of environmental stimuli, is restricted in autism. In contrast, posterior elements of the default mode network have increased spatial distribution, suggesting a 'posteriorization' of this network. These findings are consistent with a network-based model of autism, and suggest a unifying interpretation of previous work. Moreover, we provide evidence of specific abnormalities in brain network architecture underlying autism that are quantifiable using standard clinical MRI.
| S-EPMC3504046 | biostudies-literature
Project description:The goal of this work is to introduce new metrics to assess risk of Alzheimer's disease (AD) which we call AD Pattern Similarity (AD-PS) scores. These metrics are the conditional probabilities modeled by large-scale regularized logistic regression. The AD-PS scores derived from structural MRI and cognitive test data were tested across different situations using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. The scores were computed across groups of participants stratified by cognitive status, age and functional status. Cox proportional hazards regression was used to evaluate associations with the distribution of conversion times from mild cognitive impairment to AD. The performances of classifiers developed using data from different types of brain tissue were systematically characterized across cognitive status groups. We also explored the performance of anatomical and cognitive-anatomical composite scores generated by combining the outputs of classifiers developed using different types of data. In addition, we provide the AD-PS scores performance relative to other metrics used in the field including the Spatial Pattern of Abnormalities for Recognition of Early AD (SPARE-AD) index and total hippocampal volume for the variables examined.
| S-EPMC3826736 | biostudies-literature
Project description:Apathy is a neuropsychiatric condition characterized by reduced motivation, initiative, and interest in daily life activities, and it is commonly reported in several neurodegenerative disorders. The study aims to investigate large-scale brain networks involved in apathy syndrome in patients with frontotemporal dementia (FTD) and Parkinson's disease (PD) compared to a group of healthy controls (HC). The study sample includes a total of 60 subjects: 20 apathetic FTD and PD patients, 20 non apathetic FTD and PD patients, and 20 HC matched for age. Two disease-specific apathy-evaluation scales were used to measure the presence of apathy in FTD and PD patients; in the same day, a 3T brain magnetic resonance imaging (MRI) with structural and resting-state functional (fMRI) sequences was acquired. Differences in functional connectivity (FC) were assessed between apathetic and non-apathetic patients with and without primary clinical diagnosis revealed, using a whole-brain, seed-to-seed approach. A significant hypoconnectivity between apathetic patients (both FTD and PD) and HC was detected between left planum polare and both right pre- or post-central gyrus. Finally, to investigate whether such neural alterations were due to the underlying neurodegenerative pathology, we replicated the analysis by considering two independent patients' samples (i.e., non-apathetic PD and FTD). In these groups, functional differences were no longer detected. These alterations may subtend the involvement of neural pathways implicated in a specific reduction of information/elaboration processing and motor outcome in apathetic patients.
| S-EPMC8303126 | biostudies-literature
Project description:Chronic neuroinflammation, which is primarily mediated by microglia, plays an essential role in aging and neurodegeneration. It is still unclear whether this microglia-induced neuroinflammation occurs globally or is confined to distinct brain regions. In this study, we investigated microglia activity in various brain regions upon healthy aging and Alzheimer's disease (AD)-related pathology in both human and mouse samples. In purified microglia isolated from aging mouse brains, we found a profound gene expression pattern related to pro-inflammatory processes, phagocytosis, and lipid homeostasis. Particularly in white matter microglia of 24-month-old mice, abundant expression of phagocytic markers including Mac-2, Axl, CD16/32, Dectin1, CD11c, and CD36 was detected. Interestingly, in white matter of human brain tissue the first signs of inflammatory activity were already detected during middle age. Thus quantification of microglial proteins, such as CD68 (commonly associated with phagocytosis) and HLA-DR (associated with antigen presentation), in postmortem human white matter brain tissue showed an age-dependent increase in immunoreactivity already in middle-aged people (53.2 ± 2.0 years). This early inflammation was also detectable by non-invasive positron emission tomography imaging using [11C]-(R)-PK11195, a ligand that binds to activated microglia. Increased microglia activity was also prominently present in the white matter of human postmortem early-onset AD (EOAD) brain tissue. Interestingly, microglia activity in the white matter of late-onset AD (LOAD) CNS was similar to that of the aged clinically silent AD cases. These data indicate that microglia-induced neuroinflammation is predominant in the white matter of aging mice and humans as well as in EOAD brains. This white matter inflammation may contribute to the progression of neurodegeneration, and have prognostic value for detecting the onset and progression of aging and neurodegeneration.
| S-EPMC5492660 | biostudies-literature
Project description:ObjectiveDissociative experiences commonly occur in response to trauma, and while their presence strongly affects treatment approaches in posttraumatic spectrum disorders, their etiology remains poorly understood and their phenomenology incompletely characterized. Methods to reliably assess the severity of dissociation symptoms, without relying solely on self-report, would have tremendous clinical utility. Brain-based measures have the potential to augment symptom reports, although it remains unclear whether brain-based measures of dissociation are sufficiently sensitive and robust to enable individual-level estimation of dissociation severity based on brain function. The authors sought to test the robustness and sensitivity of a brain-based measure of dissociation severity.MethodsAn intrinsic network connectivity analysis was applied to functional MRI scans obtained from 65 women with histories of childhood abuse and current posttraumatic stress disorder (PTSD). The authors tested for continuous measures of trauma-related dissociation using the Multidimensional Inventory of Dissociation. Connectivity estimates were derived with a novel machine learning technique using individually defined homologous functional regions for each participant.ResultsThe models achieved moderate ability to estimate dissociation, after controlling for childhood trauma and PTSD severity. Connections that contributed the most to the estimation mainly involved the default mode and frontoparietal control networks. By contrast, all models performed at chance levels when using a conventional group-based network parcellation.ConclusionsTrauma-related dissociative symptoms, distinct from PTSD and childhood trauma, can be estimated on the basis of network connectivity. Furthermore, between-network brain connectivity may provide an unbiased estimate of symptom severity, paving the way for more objective, clinically useful biomarkers of dissociation and advancing our understanding of its neural mechanisms.
| S-EPMC8030225 | biostudies-literature
Project description:ObjectiveOur daily activities require frequent switches among competing responses at the millisecond time scale. We determined the spatiotemporal characteristics and functional significance of rapid, large-scale brain network dynamics during task switching.MethodsThis cross-sectional study investigated patients with drug-resistant focal epilepsy who played a Lumosity cognitive flexibility training game during intracranial electroencephalography (iEEG) recording. According to a given task rule, unpredictably switching across trials, participants had to swipe the screen in the direction the stimulus was pointing or moving. Using this data, we described the spatiotemporal characteristics of iEEG high-gamma augmentation occurring more intensely during switch than repeat trials, unattributable to the effect of task rule (pointing or moving), within-stimulus congruence (the direction of stimulus pointing and moving was same or different in a given trial), or accuracy of an immediately preceding response. Diffusion-weighted imaging (DWI) tractography determined whether distant cortical regions showing enhanced activation during task switch trials were directly connected by white matter tracts. Trial-by-trial iEEG analysis deduced whether the intensity of task switch-related high-gamma augmentation was altered through practice and whether high-gamma amplitude predicted the accuracy of an upcoming response among switch trials.ResultsThe average number of completed trials during five-minute gameplay was 221.4 per patient (range: 171-285). Task switch trials increased the response times, whereas later trials reduced them. Analysis of iEEG signals sampled from 860 brain sites effectively elucidated the distinct spatiotemporal characteristics of task switch, task rule, and post-error-specific high-gamma modulations. Post-cue, task switch-related high-gamma augmentation was initiated in the right calcarine cortex after 260 ms, right precuneus after 330 ms, right entorhinal after 420 ms, and bilateral anterior middle-frontal gyri after 450 ms. DWI tractography successfully showed the presence of direct white matter tracts connecting the right visual areas to the precuneus and anterior middle-frontal regions but not between the right precuneus and anterior middle-frontal regions. Task-related high-gamma amplitudes in later trials were reduced in the calcarine, entorhinal and anterior middle-frontal regions, but increased in the precuneus. Functionally, enhanced post-cue precuneus high-gamma augmentation improved the accuracy of subsequent responses among switch trials.ConclusionsOur multimodal analysis uncovered two temporally and functionally distinct network dynamics supporting task switching. High-gamma augmentation in the visual-precuneus pathway may reflect the neural process facilitating an attentional shift to a given updated task rule. High-gamma activity in the visual-dorsolateral prefrontal pathway, rapidly reduced through practice, may reflect the cost of executing appropriate stimulus-response translation.
| S-EPMC9173207 | biostudies-literature