Despite decades of advancements in diagnostic MRI, 30-50% of temporal lobe epilepsy (TLE) patients remain categorized as "non-lesional" (i.e., MRI negative or MRI-) based on visual assessment by human experts. MRI- patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI- patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that may be too subtle for the human eye to detect. This signature pattern could be successfully translated into clinical use via artificial intelligence (AI) advances in computer-aided MRI interpretation, thereby improving the detection of brain "lesional" patterns associated with TLE. Here, we tested this hypothesis by employing a three-dimensional convolutional neural network (3D CNN) applied to a dataset of 1,178 scans from 12 different centers. 3D CNN was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6) and whole-brain (78.3% ± 3.3) volumes. Our analysis subsequently focused on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI- patients from this cohort were accurately identified as TLE 82.7% ± 0.9 of the time, an encouraging finding since clinically these were all patients considered to be MRI- (i.e., not radiographically different than controls). The saliency maps from the CNN revealed that limbic structures, particularly medial temporal, cingulate, and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI+ and MRI- TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI- patients are on the same continuum common across all TLE patients. As such, AI can identify TLE lesional patterns and AI-aided diagnosis has the potential to greatly enhance the neuroimaging diagnosis of TLE and redefine the concept of "lesional" TLE.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease, with most sporadic cases lacking clear genetic causes. Abnormal pre-mRNA splicing is a fundamental mechanism in neurodegenerative diseases. For example, TAR DNA-binding protein 43 (TDP-43) loss-of-function (LOF) causes widespread RNA mis-splicing events in ALS. Additionally, splicing mutations are major contributors to neurological disorders. However, the role of intronic variants driving RNA mis-splicing in ALS remains poorly understood. To address this, we developed Spliformer to predict RNA splicing. Spliformer is a transformer-based deep learning model trained and tested on splicing events from the GENCODE database, as well as RNA-seq data from blood and central nervous system tissues. We benchmarked Spliformer against SpliceAI and Pangolin using testing datasets and paired whole-genome sequencing (WGS) with RNA-seq data. We further developed the Spliformer-motif model to identify splicing regulatory motifs. We analyzed Clinvar dataset to identify the link of splicing variants with disease pathogenicity. Additionally, we analyzed WGS data of ALS patients and controls to identify common intronic splicing variants linked to ALS risk or disease phenotypes. We also profiled rare intronic splicing variants in ALS patients to identify known or novel ALS-associated genes. Minigene assays were employed to validate candidate splicing variants. Finally, we measured spine density in neurons with a specific gene knockdown or those expressing a TDP-43 disease-causing mutant. Spliformer accurately predicts the possibilities of a nucleotide within a pre-mRNA sequence being a splice donor, acceptor, or neither. Spliformer outperformed SpliceAI and Pangolin in both speed and accuracy in tested splicing events and/or paired WGS/RNA-seq data. Spliformer-motif successfully identified canonical and novel splicing regulatory motifs. In Clinvar dataset, splicing variants are highly related to disease pathogenicity. Genome-wide analyses of common intronic splicing variants nominated one variant linked to ALS progression. Deep learning analyses of WGS data from 1,370 ALS patients revealed rare splicing variants in reported ALS genes (such as PTPRN2 and CFAP410, validated through minigene assays and RNA-seq), and TDP-43 LOF related RNA mis-splicing genes (such as PTPRD). Further genetic analysis and minigene assays nominated PCP4 and TMEM63A as ALS-associated genes. Functional assays demonstrated that PCP4 is critical for maintaining spine density and can rescue spine loss in neurons expressing a disease-causing TDP-43 mutant. In summary, we developed Spliformer and Spliformer-motif that accurately predict and interpret pre-mRNA splicing. Our findings highlight an intronic genetic mechanism driving RNA mis-splicing in ALS and nominate PCP4 as an ALS-associated gene.

Epilepsy is a network disorder, involving neural circuits at both the micro- and macroscale. While local excitatory-inhibitory imbalances are recognized as a hallmark at the microscale, the dynamic role of distinct neuron types during seizures remain poorly understood. At the macroscale, interactions between key nodes within the epileptic network, such as the central median thalamic nucleus (CMT), are critical to the, hippocampal epileptic process. However, precise mechanisms underlying these interactions remain unclear. In this study, we investigated the microcircuit dynamics within the seizure onset zone and secondary spreading regions, as well as the network connectivity between the hippocampus and the CMT, using a 4-aminopyridine (4-AP) induced hippocampal seizure model. Rats were allocated into three experimental groups. The first group used a 3D tetrode array to monitor hippocampal seizure activity and microcircuit dynamics, including seizure propagation across the macroscale network. In the second group, a chemical lesion was induced in the CMT to assess its impact on hippocampal seizures. In the third group, chemogenetic techniques were used to selectively suppress pyramidal neurons in the CMT and observe changes in neural network connectivity between the CMT and hippocampus during seizures. Offline single-unit sorting was performed using KlustaKwik and further analysis was conducted with CellExplorer. At seizure onset, the narrow interneurons exhibited increased firing rates, initiating recruitment of other neurons, followed by increased activity in pyramidal neuron. Wide interneurons also showed heightened activity subsequent to pyramidal neurons. Interneurons played a more prominent role in the microcircuit during seizures compared to baseline. The CMT exhibited characteristic seizure activity and a decrease in narrow interneuron activity, whereas the cortex did not display seizure activity during hippocampal seizures. Lesioning the CMT resulted in the loss of the tonic component of hippocampal seizures and reduced overall neuronal activity in the hippocampal. Selective suppression of CMT pyramidal neurons resulted in shortened hippocampal seizures while preserving the tonic component. Narrow interneuron activity remained unchanged, while pyramidal neuron and wide interneuron activity significantly decreased. Our findings underscore the critical role of interneurons in the micronetwork of the seizure onset zone and secondary spreading region. Narrow interneurons were particularly vital in seizure initiation, whereas wide interneurons may contribute to seizure termination within the onset zone but not in the secondary spreading region. Pyramidal neurons in the CMT influence hippocampal seizures by modulating of both hippocampal pyramidal neurons and wide interneurons.

Nerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46,802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular, and tibial nerves. Wavelet coefficient statistics, onset age, sex, and BMI were features for training a Gradient Boosting Machine model on 40,095 (5,329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis (IBM), cervical radiculopathy, lumbar radiculopathy, or peripheral neuropathy which can mimic ALS symptoms. Factors affecting survival were estimated through cox proportional hazards regression. The model trained using wavelet-features on the full waveform had 90% recall, 87% precision, and 88% accuracy. Similar model performance was measured using features only from the M-Wave or F-Wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (p<0.001, ANOVA, Tukey's post-hoc), Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient's likelihood of ALS and their survival risks. Although the model can make predictions at specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy. These probabilities provide clinicians with additional information they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians diagnose ALS sooner and manage treatment based on estimated survival, which may improve outcomes and patients' quality of life.

Post-stroke aphasia is a network disorder characterized by language impairments and aberrant network activation. While patients with post-stroke aphasia recover over time, the dynamics of the underlying changes in the brain remain elusive. Neuroimaging work demonstrated that language recovery is a heterogeneous process, characterized by varying activation levels in several regions of the left-hemispheric language network and the domain-general bilateral multiple-demand network. Crucially, this activation seems to depend on the time elapsed since stroke and the lesion location. Yet, beyond task-related brain activation, the degree and nature of interactions between regions of the language and the multiple-demand network are not well understood. In this longitudinal functional neuroimaging study, we characterized task-related functional interactions between regions of the language and the multiple-demand network during language processing. We hypothesized that interactions between language regions and between language and multiple-demand regions should change over time and depend on lesion location. We compared changes in effective connectivity in patients with left-hemispheric frontal or temporo-parietal stroke (n=17 per group) and healthy controls (n=17) with Dynamic Causal Modelling. All patients repeatedly underwent an auditory sentence comprehension paradigm during functional neuroimaging in the acute (≤ 1 week), subacute (1-2 weeks), and chronic (> 6 months) phases after stroke. We found overall increased task-related connectivity from regions of the multiple-demand to the language network across patients, resembling the principal pattern of task-related interactions in controls. Early facilitation from multiple-demand to language regions correlated with later language improvement across both groups. Crucially, recruitment of specific connections from regions of the multiple-demand to language network depended on lesion location and changed over time. In the chronic phase, patients with frontal stroke showed facilitatory modulation from the right dorsolateral prefrontal cortex, while patients with temporo-parietal stroke integrated the supplementary motor area/dorsal anterior cingulate cortex. Besides this across-network reorganization, facilitatory connectivity between regions of the language network emerged in all patients in the subacute phase. Additionally, patients with frontal stroke showed facilitatory influences from the right lesion homologue to the remaining undamaged left inferior frontal cortex in the acute phase. Collectively, we provide first evidence that functional interactions of regions within and across the language and the multiple-demand network facilitate aphasia recovery. The identified dynamic reorganization principles over the time course of recovery may inform the future use of personalized treatment protocols with neurostimulation in aphasia rehabilitation. These protocols should be tailored to the individual lesion location and recovery phase.

The somato-cognitive action network (SCAN) consists of three nodes interspersed within Penfield's motor effector regions. The configuration of the somato-cognitive action network nodes resembles the one of the 'plis de passage' of the central sulcus: small gyri bridging the precentral and postcentral gyri. Thus, we hypothesize that these may provide a structural substrate of the somato-cognitive action network. Here, using microdissections of sixteen human hemispheres, we consistently identified a chain of three distinct plis de passage with increased underlying white matter, in locations analogous to the somato-cognitive action network nodes. We mapped localizations of plis de passage into standard stereotactic space to seed fMRI connectivity across 9,000 resting-state fMRI scans, which demonstrated the connectivity of these sites with the somato-cognitive action network. Intraoperative recordings during direct electrical central sulcus stimulation further identified inter-effector regions corresponding to plis de passage locations. This work provides a critical step towards improved understanding of the somato-cognitive action network in both structural and functional terms. Further, our work has the potential to guide the development of refined motor cortex stimulation techniques for treating brain disorders, and operative resective techniques for complex surgery of the motor cortex.

Cortical spreading depolarization (CSD), the neurophysiological event believed to underlie aura, may trigger migraine headaches through inflammatory signaling that originates in neurons and spreads to the meninges via astrocytes. Increasing evidence from studies on rodents and migraine patients supports this hypothesis. The transition from pro-inflammatory to anti-inflammatory mechanisms is crucial for resolving inflammation. However, the resolution of inflammation in the context of CSD and migraine headaches remains poorly understood. This study aims to elucidate the progression of post-CSD inflammatory signaling and its resolution in neurons, astrocytes, and microglia in mouse brains. CSD was triggered optogenetically or by pinprick. HMGB1 release, caspase-1 activation, and cell-specific activation of NF-κB pairs, along with ensuing transcriptomic changes, were evaluated using immunofluorescence, Western blotting, co-immunoprecipitation, FRET analysis, and cell-specific transcriptomics. Our findings indicate that after the initial burst, HMGB1 release from neurons ceased, and caspase-1 activation, which peaked 1-hour post-CSD, diminished within 3-5 hours. This suggests that pro-inflammatory stimuli driving inflammatory signaling decreased within hours after CSD. Pro-inflammatory NF-κB p65:p50 pairs, along with anti-inflammatory cRel:p65 pairs, were detected in astrocyte nuclei shortly after CSD. However, 24 hours post-CSD, the former had disappeared while the latter persisted, indicating a shift from pro-inflammatory to anti-inflammatory activity in astrocytes. Pathway analysis of cell-specific transcriptomic data confirmed NF-κB-related pro-inflammatory transcription in astrocytes 1-hour post-CSD, while no such activity was observed in neurons. Detailed transcriptomic analysis with Bayesian cell proportion reconstruction revealed that microglia exhibited transcriptional changes trending towards an anti-inflammatory profile, along with upregulation of several chemokines and cytokines (e.g., TNF). This suggests that microglia may play a role in supporting the inflammatory responses in astrocytes through the release of these mediators. The upregulation of genes involved in chemotaxis (e.g., Ccl3) and spine pruning (e.g., C1q) in microglia implies that microglia may contribute to synaptic repair, while inflammatory signaling in astrocytes could potentially modulate meningeal nociceptor activity through an extensive astrocyte endfeet syncytium abutting subarachnoid and perivascular spaces although direct evidence remains incomplete. This nuanced understanding of the inflammatory response in CNS cell types highlights the intricate cellular interactions and responses to CSD. Following a single CSD, distinct transcriptomic responses occur in neurons, astrocytes, and microglia, driving inflammatory and anti-inflammatory responses, potentially contributing to headache initiation and resolution.

Computational neuropsychiatry is a leading discipline to explain psychopathology in terms of neuronal message passing, distributed processing, and belief propagation in neuronal networks. Active Inference (AI) has been one of the ways of representing this dysfunctional signal processing. It involves that all neuronal processing and action selection can be explained by maximizing Bayesian model evidence, or minimizing variational free energy. Following these principles, it has been suggest that dysconnection in neuronal network results in aberrant belief updating and erroneous inference, leading to psychiatric and neurologic symptoms. However, there is a classic distinction between disorders of inference (or synaptopathy - including the majority of psychiatric disorders), and disorders of brain function (including vascular neurological pathologies and severe forms of tauopathy and synucleinopathies). This distinction is generally based on the idea that synaptopathies impair neuromodulatory precision weighting, leading to rigid inferences or heightened sensitivity to noise, while disorders of brain function are linked to damage in the nervous system (disconnection). This makes it challenging to apply the logic of the free energy principle. We suggest that this distinction will enable future models of neuropsychiatric symptoms to be improved by considering more than neuronal message passing.

Although the pathophysiology of migraine involves a complex ensemble of peripheral and central nervous system changes that remain incompletely understood, the activation and sensitization of the trigeminovascular system is believed to play a major role. However, non-invasive, in vivo neuroimaging studies investigating the underlying neural mechanisms of trigeminal system abnormalities in human migraine patients are limited. Here, we studied 60 patients with migraine (55 females, mean age ± SD: 36.28 ± 11.95 years) and 20 age-/sex-matched healthy controls (19 females, mean age ± SD: 35.45 ± 13.30 years) using ultra-high field 7 Tesla diffusion tensor imaging and functional MRI, as well as PET with the translocator protein ligand [11C]-PBR28. We evaluated MRI diffusivity measures and PET signal at the trigeminal nerve root, as well as brainstem functional MRI response to innocuous, ophthalmic trigeminal nerve territory stimulation. Patients with migraine demonstrated altered white matter microstructure at the trigeminal nerve root (n=53), including reduced fractional anisotropy, compared to healthy controls (n=18). Furthermore, in patients, lower fractional anisotropy was accompanied by 1) higher neuroinflammation (i.e. elevated [11C]-PBR28 PET signal) at the nerve root (n=36) and 2) lower functional MRI activation in an ipsilateral pontine cluster consistent with spinal trigeminal nucleus (n=51). These findings were more robust on the right side, which was consistent with the observation that right headache dominant patients demonstrated higher migraine severity compared to left headache dominant patients in our cohort. Multimodal imaging of the integrated neural mechanisms that characterize migraine underscores the importance of trigeminal system remodeling as both a key aspect of the dynamics underlying migraine pathophysiology and a target for therapeutic interventions.

Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN) is a neurodegenerative disease caused by mutations in the gene encoding transthyretin (TTR). Despite amyloid deposition being pathognomonic for diagnosis, this pathology in nervous tissues cannot fully account for nerve degeneration, implying additional pathophysiology for neurodegeneration, which, however, has not yet been fully elucidated. In this study, neuroinflammation in ATTRv-PN was investigated by examining nerve morphometry, the blood-nerve barrier, and macrophage infiltration in the sural nerves of ATTRv-PN patients and the sciatic nerves of a complementary mouse system, i.e. the humanized knock-in hTTRA97S mice. The direct effects of mutant TTR proteins were evaluated in these hTTRA97S mice and a human umbilical vein endothelial cell (HUVEC) model in vivo and in vitro, respectively. This case-control and cross-sectional study included 19 patients (17 men; 62.9 ± 3.9 years; FAP stage 1, n=11; FAP stage 2, n=7; FAP stage 3, n=1) with p.Ala117Ser (A97S) and 46 patients (39 men; 65.3 ± 11.4 years; FAP stage 1, n=31; FAP stage 2, n=12; FAP stage 3, n=3) with p.Val50Met (V30M). Both genotypes had elevated protein in the cerebrospinal fluid: 88.9% (16 cases in 18 patients) in A97S and 51.1% (23 cases in 45 patients) in V30M. The myelinated nerve fibers in sural nerves were markedly depleted in ATTRv-PN and the myelinated nerve fiber density was inversely correlated with CSF protein, implying leakage of the blood-nerve barrier. The tight junction ultrastructure of the endoneurial microvessels in sural nerves was impaired, as indicated by the reduced expression of zonula occludens-1 (ZO-1). The cultured HUVEC that were not transfected with any TTR gene variant presented reduced ZO-1 expression when exposed to mutant recombinant TTR of A97S or V30M compared to wild-type TTR. Increased infiltration of macrophage with expression of inflammasome maker, NLR family pyrin domain containing 3 (NLRP3), suggested polarization to proinflammatory M1 lineage were robust in the sural nerves of ATTRv-PN patients and the sciatic nerves of hTTRA97S mice compared with those of controls and wild-type mice. In parallel, the mRNA expression of interleukin 1β was greater in the sural nerves of ATTRv-PN than in those of the controls. In conclusion, the disrupted blood-nerve barrier due to mutant TTR protein resulting in increased CSF protein level was associated with nerve degeneration in ATTRv-PN via the infiltration of inflammatory macrophages and the production of inflammatory cytokines.

Plasma phosphorylated tau biomarkers open unprecedented opportunities for identifying carriers of Alzheimer's disease pathophysiology in early disease stages using minimally invasive techniques. Plasma p-tau biomarkers are believed to reflect tau phosphorylation and secretion. However, it remains unclear to what extent the magnitude of plasma p-tau abnormalities reflects neuronal network disturbance in the form of cognitive impairment. To address this question, we included 103 cognitively unimpaired elderly and 40 cognitively impaired, amyloid-β positive individuals from the TRIAD cohort, as well as 336 cognitively unimpaired and 216 cognitively impaired, amyloid-β positive older adults from the BioFINDER-2 cohort. Participants had tau PET scans, amyloid PET scans or amyloid CSF, p-tau217, p-tau181 and p-tau231 blood measures, structural T1-MRI and cognitive assessments. In this cross-sectional study, we used regression models and correlation analyses to assess the relationship between plasma biomarkers and cognitive scores. Furthermore, we applied receiver operating characteristic curves to assess cognitive impairment across plasma biomarkers. Finally, we categorized participants into amyloid (A), p-tau (T1), and tau PET (T2) positive (+) or negative (-) profiles and ran nonparametric comparisons to assess differences across cognitive domains. We found that plasma p-tau217 was more associated with cognitive performance than p-tau181 and p-tau231, and that this relationship was particularly strong for memory scores (TRIAD: βp-tau217=-0.53; βp-tau181=-0.35; βp-tau231=-0.24; BioFINDER-2: βp-tau217=-0.52; βp-tau181=-0.24; βp-tau231=-0.29). Associations in amyloid-β positive participants resembled these results, but other cognitive scores also showed strong associations in cognitively impaired individuals. Moreover, plasma p-tau217 outperformed plasma p-tau181 and plasma p-tau231 in identifying memory impairment (Area Under the Curve values for TRIAD: p-tau217=0.86, p-tau181=0.77, p-tau231=0.75; Area Under the Curve values for BioFINDER-2: p-tau217=0.86, p-tau181=0.76, p-tau231=0.81), and in identifying executive function impairment only in the BioFINDER-2 cohort (p-tau217=0.82, p-tau181=0.76, p-tau231=0.76). Lastly, we showed that subtle memory deficits were present in A+T1+T2- participants for plasma p-tau217 (p=0.007) and plasma p-tau181 (p=0.01) in the TRIAD cohort, and for all biomarkers across cognitive domains in A+T1-T2- and A+T1+T2- individuals (p<0.001 in all) in the BioFINDER-2 cohort. A+T1+T2+ individuals showed cognitive deficits in both cohorts (p<0.001 in all). Together, our results suggest that plasma p-tau217 stands out as a biomarker capable of identifying memory deficits due to Alzheimer's disease and that memory impairment certainly occurs in amyloid and plasma p-tau positive individuals that have no significant amounts of tau in the neocortex.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographic disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in NIHSS at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers like thrombectomy success, final infarct localization, and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalised linear model. Our deep-learning model showed significant superiority, with a mean Dice score of 0.48 on internal (n = 50) and 0.52 on external (n = 51) test data, versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischemia. We believe this method holds significant potential to enhance personalised therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.

The neurobiological mechanisms driving the ictal-interictal fluctuations and the chronification of migraine remain elusive. We aimed to construct a composite genetic-microRNA model that could reflect the dynamic perturbations of the disease course and inform the pathogenesis of migraine. We prospectively recruited four groups of participants, including interictal episodic migraine (i.e., headache-free for > 72 hrs apart from prior and subsequent attacks), ictal episodic migraine (i.e., during moderate to severe migraine attacks), chronic migraine, and controls in the discovery cohort. Next-generation sequencing (NGS) was used for microRNA profiling. The candidate microRNAs were validated with quantitative PCR (qPCR) in an independent validation cohort. Biological pathways associated with the microRNA regulome and interaction networks were explored. In addition, all participants received genotyping with the Axiom Genome-Wide Array TWB chip. A composite model was established, combining disease-associated microRNAs and genetic risk scores (GRS) indicative of genetic susceptibility, with the objective of differentiating migraine from controls using a binary outcome. From a total of 120 participants in the discovery cohort and 197 participants in the validation cohort, we identified disease-state microRNA signatures (including miR-183, miR-25, and miR-320) that were ubiquitously higher or lower in patients with migraine compared to controls. We have also validated four disease-activity miRNA signatures (miR-1307-5p, miR-6810-5p, let-7e, and miR-140-3p) that were differentially expressed only during the ictal stage of episodic migraine. Functional analysis suggested that prolactin and estrogen signaling pathways might play important roles in the pathogenesis. Moreover, the composite microRNA-GRS model differentiated patients from controls, achieving a positive predictive value of over 90%. To conclude, we developed a composite microRNA-genetic risk score model, which may serve as a predictive tool for identifying high-risk individuals. Our findings may help illuminate potential pathogenic mechanisms underlying the dysfunctional allostasis of migraine and pave the way for future precision medicine.

Prions are assemblies of misfolded prion protein that cause several fatal and transmissible neurodegenerative diseases, with the most common phenotype in humans being sporadic Creutzfeldt-Jakob disease (sCJD). Aside from variation of the prion protein itself, molecular risk factors are not well understood. Prion and prion-like mechanisms are thought to underpin common neurodegenerative disorders meaning that the elucidation of mechanisms could have broad relevance. Herein we sought to further develop our understanding of the factors that confer risk of sCJD using a systematic gene prioritization and functional interpretation pipeline based on multiomic integrative analyses. We integrated the published sCJD genome-wide association study (GWAS) summary statistics with publicly available bulk brain and brain cell type gene and protein expression datasets. We performed multiple transcriptome and proteome-wide association studies (TWAS & PWAS) and Bayesian genetic colocalization analyses between sCJD risk association signals and multiple brain molecular quantitative trait loci signals. We then applied our systematic gene prioritization pipeline on the obtained results and nominated prioritized sCJD risk genes with risk-associated molecular mechanisms in a transcriptome and proteome-wide manner. Genetic upregulation of both gene and protein expression of syntaxin-6 (STX6) in the brain was associated with sCJD risk in multiple datasets, with a risk-associated gene expression regulation specific to oligodendrocytes. Similarly, increased gene and protein expression of protein disulfide isomerase family A member 4 (PDIA4), involved in the unfolded protein response, was linked to increased disease risk, particularly in excitatory neurons. Protein expression of mesencephalic astrocyte derived neurotrophic factor (MANF), involved in protection against endoplasmic reticulum stress and sulfatide binding (linking to the enzyme in the final step of sulfatide synthesis, encoded by sCJD risk gene GAL3ST1), was identified as protective against sCJD. In total 32 genes were prioritized into two tiers based on the level of evidence and confidence for further studies. This study provides insights into the genetically-associated molecular mechanisms underlying sCJD susceptibility and prioritizes several specific hypotheses for exploration beyond the prion protein itself and beyond the previously highlighted sCJD risk loci through the newly prioritized sCJD risk genes and mechanisms. These findings highlight the importance of glial cells, sulfatides and the excitatory neuron unfolded protein response in sCJD pathogenesis.

Perception integrates external sensory signals with internal predictions that reflect prior knowledge about the world. Previous research suggests that this integration is governed by slow alternations between an external mode, driven by sensory signals, and an internal mode, shaped by prior knowledge. Using a double-blind, placebo-controlled, cross-over experiment in healthy human participants, we investigated the effects of the N-Methyl-D-aspartate receptor (NMDAR) antagonist S-ketamine on the balance between external and internal modes. We found that S-ketamine causes a shift of perception toward the external mode. A case-control study revealed that individuals with paranoid Scz, a disorder repeatedly associated with NMDAR hypofunction, spend more time in the external mode. This NMDAR-dependent increase in the external mode suggests that the symptoms of schizophrenia are caused by recurring dissociations of perception from prior knowledge about the world.

Resective epilepsy surgery can be an effective treatment for patients with medication-resistant focal epilepsy. Epilepsy resection consists of the surgical removal of an epileptic focus to stop seizure generation and disrupt the epileptic network. However, even focal surgical resections for epilepsy lead to widespread brain network changes. Understanding the impact of epilepsy surgery on the brain is crucial to improve surgical outcomes for patients. Here we provide a summary of studies imaging the postsurgical effects of epilepsy resection on the brain. We focus on MRI and PET studies of temporal lobe and pediatric epilepsy, reflecting the current literature. We discuss three potential mechanisms for surgery-induced brain changes: damage and degeneration, recovery, and reorganization. We additionally review the postsurgical brain correlates of surgical outcomes as well as the potential to predict the impact of surgery on an individual patient's brain. A comprehensive characterization of the impact of surgery on the brain and precise methods to predict these brain network changes could lead to more personalized surgeries that improve seizure outcomes and reduce neuropsychological deficits after surgery.

Seizures in people with dementia (PWD) are associated with faster cognitive decline and worse clinical outcomes. However, the relationship between ongoing seizure activity and postmortem neuropathology in PWD remains unexplored. We compared post-mortem findings in PWD with active, remote, and no seizures using multicentre data from 39 Alzheimer's Disease Centres from 2005 to 2021. PWD were grouped by seizure status into active (seizures over the preceding one year), remote (prior seizures but none in the preceding year), and no seizures (controls). Baseline demographics, cognition, mortality, and postmortem findings of primary and contributing(co-pathologies) Alzheimer's Disease(AD), Frontotemporal lobar degeneration(FTD), Lewy body, vascular pathologies and neurodegeneration were compared among the groups using Pearson's Chi-squared test, fisher's exact test, t-test, and ANOVA tests. Of 10,474 deceased PWD, active seizure participants suffered the highest mortality among the groups(proportion deceased among the groups: active=56%remote=35%, controls=34%, p<0.001). Among 6085 (58.1% of deceased) who underwent autopsy, 294 had active, 151 had remote, and 5640 had no seizures. PWD and active seizures died at a younger age (Active=75.8, remote=77.9, controls: 80.8 years, p <0.001) and had more severe dementia (CDR-Global: active=2.36, remote=1.90, controls=1.69, p<0.001). In post hoc analyses, those with primary postmortem diagnosis of AD with active seizures had more severe and later stages of AD pathology and ATN (amyloid, tau, and neurodegeneration) as evidenced by Braak stage for neurofibrillary(tau) degeneration and CERAD score density of neuritic(amyloid) plaques than remote seizure participants and controls. Active seizure participants had more neurodegeneration, evidenced by cerebral atrophy, hippocampal atrophy, and locus coeruleus hypopigmentation than controls. Among participants with primary postmortem diagnosis of non-AD, in posthoc analyses, active seizure participants had worse AD co-pathology evidenced by higher Braak stages than remote seizures and controls and a higher thal phase of beta-amyloid plaques than controls. Neurodegeneration (cerebral/hippocampal atrophy) and LC hypopigmentation were comparable among the groups. In both primary postmortem AD and non-AD diagnoses, FTD (co)pathology was less prevalent among active seizure participants than controls, while vascular pathology, Circle of Willis atherosclerosis, Lewy body pathology, lobar atrophy, and substantia nigra hypopigmentation were comparable among the three groups. This study shows that active, compared to remote seizures, are associated with earlier death and postmortem evidence of more severe ATN pathology. Active seizures are associated with more advanced AD pathology in AD and worse AD co-pathology in non-AD dementias. Therefore, clinicians should be vigilant in detecting ongoing seizures as this could reflect a worse prognosis in PWD.

Lewy body (LB) pathology is present as a co-pathology in approximately 50% of Alzheimer's disease (AD) dementia patients and may even represent the main neuropathologic substrate in a subset of patients with amnestic impairments. However, the degree to which LB pathology affects the neurodegenerative course and clinical phenotype in amnestic patients is not well understood. Recently developed α-synuclein seed amplification assays (αSyn-SAAs) provide a unique opportunity for further investigating the complex interplay between AD and LB pathology in shaping heterogeneous regional neurodegeneration patterns and clinical trajectories among amnestic patients. We studied 865 patients from the ADNI cohort with clinical diagnoses of aMCI (N=661) or AD dementia (N=211), who had CSF and FDG-PET data available. CSF samples were analyzed for peptide levels of Aβ1-42 and p-tau181, and αSyn positivity was evaluated using a novel αSyn-SAA. Based on positive/negative results on the different biomarkers, subjects were classified as "AD-LB-" (N=304), "AD+LB-" (N=335), "AD+LB+" (N=158) and "AD-LB+" (N=68). We analyzed group differences in regional FDG-PET patterns, demographics, APOE4 genotype, baseline and longitudinal domain-specific cognitive profiles (memory vs executive function/visuospatial performance), as well as risk for developing hallucinations. AD+LB+ showed worse global cognition (MMSE: p=0.005) and declined faster (p<0.001) than AD+LB-, but both groups exhibited similar memory-predominant cognitive profiles. In FDG-PET, AD+LB+ showed more severe hypometabolism compared to AD+LB-, but both groups were characterized by largely identical patterns of temporo-parietal hypometabolism. By contrast, AD-LB+ were less globally impaired (p<0.001) but characterized by a markedly more dysexecutive and visuospatial profile (p<0.003) and a strikingly different posterior-occipital pattern of hypometabolism. APOE4 positivity was similar between AD+LB+ and AD+LB- (72% vs. 75%, p=0.28) but lower in AD-LB+ (28%, p<0.001). On a group level, AD+LB+, AD+LB-, and AD-LB+ showed similar risks of developing hallucinations, but patients with a LB-like posterior-occipital hypometabolism pattern had a significantly higher risk compared to those showing an AD-typical temporo-parietal pattern (HR=2.58, p=0.004). In conclusion, LB co-pathology in AD was associated with more severe hypometabolism and faster cognitive decline, but did not affect the regional hypometabolic pattern or cognitive profile. By contrast, patients with relatively pure LB pathology showed a more executive/visuospatial-predominant cognitive profile and a distinct posterior-occipital hypometabolism pattern characteristic for LB disease. These findings indicate that the presence of LB pathology may have different consequences for the clinical phenotype depending on AD co-morbidity, which may have critical implications for accurate diagnosis and prognosis of patients presenting with amnestic syndromes.