Extensive neuroimaging research in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) has identified brain atrophy as a disease phenotype. While it is also related to a complex genetic architecture, the transition from genetic risk factors to brain vulnerabilities remains unclear. Using a population-based approach, we examined the associations between epilepsy-related polygenic risk for HS (PRS-HS) and brain structure in healthy developing children, assessed their relation to brain network architecture, and evaluated its correspondence with case-control findings in TLE-HS diagnosed patients relative to healthy individuals. We used genome-wide genotyping and structural T1-weighted MRI of 3826 neurotypical children from the Adolescent Brain Cognitive Development (ABCD) study. Surface-based linear models related PRS-HS to cortical thickness measures, and subsequently contextualized findings with structural and functional network architecture based on epicentre mapping approaches. Imaging-genetic associations were then correlated to atrophy and disease epicentres in 785 patients with TLE-HS relative to 1512 healthy controls aggregated across multiple sites. Higher PRS-HS was associated with decreases in cortical thickness across temporo-parietal as well as fronto-central regions of neurotypical children. These imaging-genetic effects were anchored to the connectivity profiles of distinct functional and structural epicentres. Compared with disease-related alterations from a separate epilepsy cohort, regional and network correlates of PRS-HS strongly mirrored cortical atrophy and disease epicentres observed in patients with TLE-HS and were highly replicable across different studies. Findings were consistent when using statistical models controlling for spatial autocorrelations and robust to variations in analytic methods. Capitalizing on recent imaging-genetic initiatives, our study provides novel insights into the genetic underpinnings of structural alterations in TLE-HS, revealing common morphological and network pathways between genetic vulnerability and disease mechanisms. These signatures offer a foundation for early risk stratification and personalized interventions targeting genetic profiles in epilepsy.

Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI negative) based on visual assessment by human experts. MRI-negative patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI-negative patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that might be too subtle for the human eye to detect. This signature pattern could be translated successfully into clinical use via advances in artificial intelligence in computer-aided MRI interpretation, thereby improving the detection of brain 'lesional' patterns associated with TLE. Here, we tested this hypothesis by using a three-dimensional convolutional neural network applied to a dataset of 1178 scans from 12 different centres, which 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 focused subsequently on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI-negative patients from this cohort were accurately identified as TLE 82.7% ± 0.9% of the time, an encouraging finding given that clinically these were all patients considered to be MRI negative (i.e. not radiographically different from controls). The saliency maps from the convolutional neural network 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-positive and MRI-negative TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI-negative patients are on the same continuum common across all TLE patients. As such, artificial intelligence can identify TLE lesional patterns, and artificial intelligence-aided diagnosis has the potential to enhance the neuroimaging diagnosis of TLE greatly and to redefine the concept of 'lesional' TLE.

INTRODUCTION:Sleep disturbances in Parkinson's disease (PD) are extremely common and significantly impact quality of life. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established therapy for motor symptoms and may also influence sleep outcomes. However, the relationship between stimulation location, structural networks, and sleep improvements remains poorly understood, particularly over longer durations. METHODS:We conducted a retrospective analysis of 53 PD patients implanted with bilateral STN-DBS. Sleep outcomes were assessed using self-report measures, with subsection scores for nighttime sleep, daytime wakefulness, and total sleep disturbance analysed. Local stimulation effects were modelled using sweet spot analyses, and innervated tracts were examined through discriminative fiber filtering. Associations with motor improvements and demographic factors were analysed. RESULTS:Significant improvements were reported in both nighttime sleep and daytime wakefulness. Motor symptom improvements were significantly positively associated with cumulative sleep scores but did not fully explain sleep changes. Sweet spot analyses revealed associations between cumulative sleep score improvements and the sensorimotor STN, while fiber filtering implicated brainstem, cerebellar, and cortical motor tracts for daytime and cumulative sleep improvements. These associations remained significant after controlling for motor improvements. DISCUSSION:Our findings suggest that STN-DBS may modulate sleep via broader structural connections and beyond motor symptom improvements. The lateralised effects and involvement of widespread tracts underscore the complexity of DBS-related sleep modulation. Future studies should incorporate longitudinal designs and objective measures to disentangle motor-independent mechanisms and optimise therapeutic strategies.

Structural neuroimaging analyses require "research quality" images, procured with costly MRI acquisitions. Isotropic (3D-T1) images are desirable for quantitative analyses, however, a routine compromise in the clinical setting is to acquire anisotropic (2D-T1) analogues for qualitative visual inspection. ML (machine learning-based) software have shown promise in addressing some of the limitations of 2D-T1 scans in research applications, yet their efficacy in quantitative research is generally poorly understood. Quantitative morphometric analyses have previously identified pathology-related abnormalities of the subcortical structures in idiopathic generalised epilepsy (IGE), which have been overlooked based on visual inspection. As such, IGE biomarkers present a suitable model in which to evaluate the applicability of image preprocessing methods. This study, therefore, explores subcortical structural biomarkers of IGE, first in our "silver standard" 3D-T1 scans, then in 2D-T1 scans that were either untransformed, resampled using a classical interpolation approach, or synthesised with a resolution and contrast agnostic ML model. 2D-T1 and 3D-T1 MRI scans were acquired during the same scanning session for 33 individuals with drug-sensitive IGE (age mean 32.16 ± SD = 14.20, male n = 14) and 42 individuals with drug-resistant IGE (31.76 ± 11.12, 17), all diagnosed at the Walton Centre NHS Foundation Trust Liverpool, alongside 39 age- and sex-matched healthy controls (32.32 ± 8.65, 16). The untransformed 2D-T1 scans were resampled into isotropic images using NiBabel (res-T1), and preprocessed into synthetic isotropic images using SynthSR (syn-T1). For the 3D-T1, 2D-T1, res-T1, and syn-T1 images, the recon-all command from FreeSurfer 8.0.0 was used to create parcellations of 174 anatomical regions (equivalent to the 174 regional parcellations provided as part of the DL+DiReCT pipeline), defined by the aseg and Destrieux atlases, and FSL run_first_all was used to segment subcortical surface shapes. The new ML FreeSurfer pipeline, recon-all-clinical, was also tested in the 2D-T1, 3D-T1, and res-T1 images. As a model comparison for SynthSR, the DL+DiReCT pipeline was used to provide segmentations of the 2D-T1 and res-T1 images, including estimates of regional volume and thickness. Spatial overlap and intraclass correlations between the morphometrics of the eight resulting parcellations were first determined, then subcortical surface shape abnormalities associated with IGE were identified by comparing the FSL run_first_all outputs of patients with controls. When standardised to the metrics derived from the 3D-T1 scans, cortical volume and thickness estimates trended lower for the 2D-T1, res-T1, syn-T1, and DL+DiReCT outputs, whereas subcortical volume estimates were more coherent. Dice coefficients revealed an acceptable spatial similarity between the cortices of the 3D-T1 scans and the other images overall, and was higher in the subcortical structures. Intraclass correlation coefficients were consistently lowest when metrics were computed for model-derived inputs, and estimates of thickness were less similar to the ground truth than those of volume. For the people with epilepsy, the 3D-T1 scans showed significant surface deflations across various subcortical structures when compared with those of healthy controls. Analysis of the 2D-T1 scans enabled the reliable detection of a subset of subcortical abnormalities, whereas analyses of the res-T1 and syn-T1 images were more prone to false-positive results. Resampling and ML image synthesis methods do not currently attenuate partial volume effects resulting from low through plane resolution in anisotropic MRI scans, instead quantitative analyses using 2D-T1 scans should be interpreted with caution, and researchers should consider the potential implications of preprocessing. The recon-all-clinical pipeline is promising, but requires further evaluation, especially when considered as an alternative to the classical pipeline.

OBJECTIVE:Few prospective studies exist on newly diagnosed focal epilepsy (NDFE), a critical period for understanding epilepsy's biology and identifying biomarkers and potential interventions. We report a prospective cohort study in patients with NDFE and age-, sex-, and education-matched healthy controls. METHODS:We recruited 104 patients with NDFE and 45 controls for research-grade 3 Tesla multi-modal magnetic resonance imaging (MRI), electroencephalography (EEG), comprehensive neuropsychological testing, and blood biomarker investigations. Baseline clinical, neuroradiological, MRI morphometric, and neuropsychological findings are reported in this article. RESULTS:Following neuroradiological reporting, MRI was unremarkable in 38% of patients, showed lesions associated with epilepsy in 12%, abnormalities of unknown significance in 49%, and incidental findings in 23%. For controls, these figures were 56%, 7%, 33%, and 16%, respectively. Patients had more white matter hyperintensities, classified as abnormalities of unknown significance, than controls. Reduced bihemispheric frontal lobe cortical thickness and thalamic volumes with moderate effect sizes were observed in patients. Compared to controls, patients scored lower on executive function, processing speed, and visual, delayed, and immediate memory tasks, and higher on depression and anxiety assessments. Cluster analysis identified four distinct patient cognitive profiles, two of which were associated with high levels of anxiety and depression and lower executive function and memory scores. SIGNIFICANCE:Adults with focal NDFE have more MRI-positive findings than previously reported. Subtle white matter lesions may have clinical significance and a pathophysiological basis in focal epilepsy. Morphometric and neuropsychological changes at epilepsy diagnosis suggest that brain and cognitive alterations are not solely due to chronic epilepsy.

Congenital insensitivity to pain (CIP) due to the R221W mutation on the nerve growth factor gene results in reduced peripheral C-nociceptor density and behavioural indifference to painful stimuli. While functional neuroimaging has revealed altered cortical and sub-cortical pain processing in R221W carriers, structural white matter changes remain unexplored and may suggest an anatomical basis of symptoms. Heterozygous R221W carriers’ (n = 11) and age-, sex-, education-matched controls’ (n = 11) diffusion tensor imaging data were compared using fixel-based analysis, and complimentary edge and node analyses using graph theory, and network-based statistics. Whole-brain and region of interest (ROI) fixel-based analyses revealed significantly reduced fibre density and fibre-bundle cross-section in brainstem motor tracts of R221W carriers, encompassing the corticospinal pathways, corona radiata, external capsule, cerebellar peduncles, and pontine crossing (p < 0.05). Graph theory analysis of pain-processing ROIs demonstrated reduced local efficiency in right anterior cingulate cortex (ACC) and altered betweenness centrality in bilateral insula and left ACC of R221W carriers. Despite R221W carriers showing higher node degrees in the somatosensory cortex and ACC, these connections had reduced efficiency and integration with cortical network regions. Network-based statistics identified a possible compensatory subnetwork with stronger connectivity from right thalamus to left ACC and left insula in R221W carriers (p < 0.019). These findings suggest that congenitally reduced peripheral nociception could lead to abnormalities in the thalamocortical and motor efferent pathway, but not sensory afferent pathways. The combination of reduced brainstem motor tract integrity and altered cortical network efficiency, alongside potentially compensatory thalamo-cortical connectivity, could support a model of R221W CIP as motor under-reactivity rather than sensory insensitivity. ### Competing Interest Statement The authors have declared no competing interest. Data is made freely available with CC0 licence on Figshare[95][1] and the code is freely accessible on GitHub[96][2]. [1]: #ref-95 [2]: #ref-96

Hemispheric asymmetries in white matter tracts are proposed key determinants of language lateralisation, yet evidence in healthy individuals remains inconsistent. This suggests that simple tractography techniques might not be sensitive enough to identify language dominance. Significant insights into the functional organization of the human brain may be achieved by considering networks and brain connectivity, providing more information about discrepancies in people with different hemispheric language dominance. In this study, we examined 285 healthy participants compare their structural connectomes at the whole-brain level and determine the networks responsible for the three different functional language lateralisation groups (typical, atypical and strongly atypical). Probabilistic tractography generated whole-brain tractograms, and white matter fibres were filtered according to anatomical Boolean guidelines. Connectivity matrices with nodes corresponding to supramodal sentence areas in the language atlas and edges weighted by fractional anisotropy (FA) were generated to compare the groups using graph theory and network-based statistic (NBS) approaches. We demonstrated that both atypical (bilateral) and strongly atypical (right-lateralised) lateralisation are characterised by heightened interhemispheric temporal connectivity. Post-hoc analyses showed that strongly atypical individuals exhibited increased temporo-frontal connectivity, while atypical individuals had enhanced temporal and frontal connectivity but lacked temporo-frontal connections. These connectivity patterns diverge from traditional models of hemispheric dominance, suggesting a reliance on integrated bilateral networks in atypically lateralised individuals. This reflects distinct neural mechanisms underlying atypical language organisation, departing from developmental trajectory of typical lateralisation and offering insights into cognitive flexibility and clinical applications. ### Competing Interest Statement The authors have declared no competing interest.

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres. Compared to disease-related effects derived from epilepsy case-control cohorts, structural correlates of PRS-HS mirrored atrophy and epicentre patterns in patients with TLE-HS. By identifying a potential pathway between genetic vulnerability and disease mechanisms, our findings provide new insights into the genetic underpinnings of structural alterations in TLE-HS and highlight potential imaging-genetic biomarkers for early risk stratification and personalized interventions.

Interhemispheric anatomical differences have long been thought to be related to language lateralization. Previous studies have explored whether asymmetries in the diffusion characteristics of white matter language tracts are consistent with language lateralization. These studies, typically with smaller cohorts, yielded mixed results. This study investigated whether connectomic analysis of quantitative anisotropy (QA) and shape features of white matter tracts across the whole brain are associated with language lateralization. We analyzed 1,040 healthy individuals (562 females) from the Human Connectome Project database. Hemispheric language dominance for each participant was quantified using a laterality quotient (LQ) derived from fMRI activation in regions of interest (ROIs) associated with a language comprehension task compared against a math task. A linear regression model was used to examine the relationship between structural asymmetry and functional lateralization. Connectometry revealed a significant negative correlation between LQs and QA of corpus callosum tracts, indicating that higher QA in these regions is associated with bilateral and right hemisphere language representation in frontal and temporal regions. Left language laterality in the temporal lobe was significantly associated with longer right inferior fronto-occipital fasciculus (IFOF) and forceps minor tracts. These results suggest that diffusion measures of microstructural architecture as well as geometrical features of reconstructed white matter tracts play a role in language lateralization. People with increased dependence on the right or both frontal hemispheres for language processing may have more developed commissural fibers, which may support more efficient interhemispheric communication.

ABSTRACTObjectivesTemporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments.MethodsWe studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves.ResultsIn TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions.ConclusionsThis study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Objective: There are few prospective longitudinal studies in patients with newly diagnosed epilepsy (NDE) despite that this is a key time point to understand the underlying biology of epilepsy and to identify potential interventions and biomarkers for seizure and cognitive outcomes. Here we have performed a prospective combined neuroimaging and neuropsychological study in a cohort of patients with focal NDE and healthy controls. Methods: We recruited 104 patients with NDE and 45 healthy controls for research-grade 3 Tesla MRI (diagnostic and structural imaging, diffusional kurtosis imaging, resting-state functional MRI, task-based functional MRI), EEG, comprehensive neuropsychological, and blood biomarker investigations. We report here on the baseline clinical, neuroradiological, MRI morphometric, and neuropsychological findings in this cohort. Results: 38% of patients had unremarkable MRI features, 12% had lesions of known significance in epilepsy, 49% with abnormalities of unknown significance in epilepsy, and 23% with incidental findings. In comparison, 56% of controls had unremarkable MRI features, 7% had lesions of known significance in epilepsy, 33% with abnormalities of unknown significance in epilepsy, and 16% had incidental findings. Patients had a higher incidence of white matter hyperintensities compared to controls. Reduced bihemispheric frontal lobe cortical thickness and thalamic volumes were observed in patients with moderate effect sizes. Patients scored significantly lower on tasks of executive function, processing speed, and visual, delayed, and immediate memory, and significantly higher on depression and anxiety assessments compared to controls. Patient neuropsychological performance was related to various brain morphometric features. Significance: People with adult focal NDE have a greater proportion of MRI-positive findings than previously reported. Subtle white matter lesions may represent an important diagnostic criterion and have a pathophysiological basis in focal epilepsy. Morphometric and neuropsychological alterations are present at the point of diagnosis of epilepsy, which suggests that brain and cognitive changes are not exclusively due to the deleterious impact of chronic epilepsy.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementThis work was funded by a UK Medical Research Council (MRC) research grant (MR/S00355X/1).### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:This study was approved by the Northwest Liverpool East Research Ethics Committee (19/NW/0384) through the Integrated Research Application System (Project ID 260623). The research is sponsored by the University of Liverpool (UoL001449) and UK Health Research Authority approval was provided on 22nd August 2019. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll data produced in the present study are available upon reasonable request to the authors

Convolutional neural networks (CNN) show great promise for translating decades of research on structural abnormalities in temporal lobe epilepsy into clinical practice. Three-dimensional CNNs typically outperform two-dimensional CNNs in medical imaging. Here we explore for the first time whether a three-dimensional CNN outperforms a two-dimensional CNN for identifying temporal lobe epilepsy-specific features on MRI. Using 1178 T1-weighted images (589 temporal lobe epilepsy, 589 healthy controls) from 12 surgical centres, we trained 3D and 2D CNNs for temporal lobe epilepsy versus healthy control classification, using feature visualization to identify important regions. The 3D CNN was compared to the 2D model and to a randomized model (comparison to chance). Further, we explored the effect of sample size with subsampling, examined model performance based on single-subject clinical characteristics, and tested the impact of image harmonization on model performance. Across 50 datapoints (10 runs with 5-folds each) the 3D CNN median accuracy was 86.4% (35.3% above chance) and the median F1-score was 86.1% (33.3% above chance). The 3D model yielded higher accuracy compared to the 2D model on 84% of datapoints (median 2D accuracy, 83.0%), a significant outperformance for the 3D model (binomial test: P < 0.001). This advantage of the 3D model was only apparent at the highest sample size. Saliency maps exhibited the importance of medial-ventral temporal, cerebellar, and midline subcortical regions across both models for classification. However, the 3D model had higher salience in the most important regions, the ventral-medial temporal and midline subcortical regions. Importantly, the model achieved high accuracy (82% accuracy) even in patients without MRI-identifiable hippocampal sclerosis. Finally, applying ComBat for harmonization did not improve performance. These findings highlight the value of 3D CNNs for identifying subtle structural abnormalities on MRI, especially in patients without clinically identified temporal lobe epilepsy lesions. Our findings also reveal that the advantage of 3D CNNs relies on large sample sizes for model training.

Up to 35% of individuals diagnosed with epilepsy continue to have seizures despite treatment, commonly referred to as drug-resistant epilepsy. Uncontrolled seizures can directly, or indirectly, negatively impact an individual's quality of life. To inform clinical management and life decisions, it is important to be able to predict the likelihood of seizure control. Those likely to achieve seizure control will be able to return sooner to their usual work and leisure activities and require less follow-up, whereas those with a poor prognosis will need more frequent clinical attendance and earlier consideration of epilepsy surgery. This is a systematic review aimed at identifying demographic, clinical, physiological (e.g., electroencephalographic), and imaging (e.g., magnetic resonance imaging) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). MEDLINE and Embase were searched for prediction models of treatment outcomes in patients with NDE. Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST (Prediction Model Risk of Bias Assessment Tool) tool. Baseline variables associated with treatment outcomes are reported as prognostic factors. After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally scored high for susceptibility to bias. Outcomes reported fit broadly into four categories: drug resistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics/types, epilepsy history, and age at onset. Antiseizure medication response was often included as a baseline variable, potentially obscuring other factor relationships at baseline. Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) practices. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE.

BACKGROUND:Diabetes mellitus (DM) is associated with structural grey matter alterations in the brain, including changes in the somatosensory and pain processing regions seen in association with diabetic peripheral neuropathy. In this case-controlled biobank study, we aimed to ascertain differences in grey and white matter anatomy in people with DM compared with non-diabetic controls (NDC). METHODS:This study utilises the UK Biobank prospective, population-based, multicentre study of UK residents. Participants with diabetes and age/gender-matched controls without diabetes were selected in a three-to-one ratio. We excluded people with underlying neurological/neurodegenerative disease. Whole brain, cortical, and subcortical volumes (188 regions) were compared between participants with diabetes against NDC corrected for age, sex, and intracranial volume using univariate regression models, with adjustment for multiple comparisons. Diffusion tensor imaging analysis of fractional anisotropy (FA) was performed along the length of 50 white matter tracts. RESULTS:We included 2404 eligible participants who underwent brain magnetic resonance imaging (NDC, n = 1803 and DM, n = 601). Participants with DM had a mean (±standard deviation) diagnostic duration of 18 ± 11 years, with adequate glycaemic control (HbA1C 52 ± 13 mmol/mol), low prevalence of microvascular complications (diabetic retinopathy prevalence, 5.8%), comparable cognitive function to controls but greater self-reported pain. Univariate volumetric analyses revealed significant reductions in grey matter volume (whole brain, total, and subcortical grey matter), with mean percentage differences ranging from 2.2% to 7% in people with DM relative to NDC (all p < 0.0002). The subcortical (bilateral cerebellar cortex, brainstem, thalamus, central corpus callosum, putamen, and pallidum) and cortical regions linked to sensorimotor (bilateral superior frontal, middle frontal, precentral, and postcentral gyri) and visual functions (bilateral middle and superior occipital gyri), all had lower grey matter volumes in people with DM relative to NDC. People with DM had significantly reduced FA along the length of the thalamocortical radiations, thalamostriatal projections, and commissural fibres of the corpus callosum (all; p < 0·001). INTERPRETATION:This analysis suggests that anatomic differences in brain regions are present in a cohort with adequately controlled glycaemia without prevalent microvascular disease when compared with volunteers without diabetes. We hypothesise that these differences may predate overt end-organ damage and complications such as diabetic neuropathy and retinopathy. Central nervous system alterations/neuroplasticity may occur early in the natural history of microvascular complications; therefore, brain imaging should be considered in future mechanistic and interventional studies of DM.

Patients with chronic focal epilepsy commonly exhibit subcortical atrophy, particularly of the thalamus. The timing of these alterations remains uncertain, though preliminary evidence suggests that observable changes may already be present at diagnosis. It is also not yet known how these morphological changes are linked to the coherence of white matter pathways throughout the brain, or to neuropsychological function often compromised before antiseizure medication treatment. This study investigates localized atrophy in subcortical regions using surface shape analysis in individuals with newly diagnosed focal epilepsy (NDfE) and assesses their implications on brain connectivity and cognitive function. We collected structural (T1w) and diffusion-weighted MRI and neuropsychological data from 104 patients with NDfE and 45 healthy controls (HCs) matched for age, sex, and education. A vertex-based shape analysis was performed on subcortical structures to compare patients with NDfE and HC, adjusting for age, sex, and intracranial volume. The mean deformation of significance areas (pcor < 0.05) was used to identify white matter pathways associated with overall shape alterations in patients relative to controls using correlational tractography. Additionally, the relationship between significant subcortical shape values and neuropsychological outcomes was evaluated using a generalized canonical correlation approach. Shape analysis revealed bilateral focal inward deformation (a proxy for localized atrophy) in anterior areas of the right and left thalamus and right pallidum in patients with NDfE compared to HC (FWE corrected). No structures showed areas of outward deformation in patients. The connectometry analysis revealed that fractional anisotropy (FA) was positively correlated with thalamic and pallidal shape deformation, that is, reduced FA was associated with inward deformation in tracts proximal to and or connecting with the thalamus including the fornix, frontal, parahippocampal, and corticothalamic pathways. Thalamic and pallidal shape changes were also related to increased depression and anxiety and reduced memory and cognitive function. These findings suggest that atrophy of the thalamus, which has previously been associated with the generation and maintenance of focal seizures, may present at epilepsy diagnosis and relate to alterations in both white matter connectivity and cognitive performance. We suggest that at least some alterations in brain structure and consequent impact on cognitive and affective processes are the result of early epileptogenic processes rather than exclusively due to the chronicity of longstanding epilepsy, recurrent seizures, and treatment with antiseizure medication.

Anxiety is the most common form of mental health disorder, affecting millions of people worldwide. Psychosocial interventions such as mindfulness and cognitive behavioral therapy (CBT) have been suggested as an effective treatment in the management of general anxiety and anxiety disorders, with emerging evidence also suggesting the effectiveness of hypnosis. Moreover, anxiety has shown to be linked to the onset and development of several cardiovascular diseases (CVD), which are the leading cause of global death. In this paper, we review the current literature to examine the role that anxiety has on the onset and development of CVD and summarize the current knowledge on the role that hypnosis and hypnotherapy have in reducing anxiety, also explaining how this can impact the cardiovascular system and the prevention of CVD. Review of the evidence suggests that hypnosis and hypnotherapy are effective in treating anxiety and may positively affect the heart and the cardiovascular system, reducing sympathetic activation and increasing parasympathetic tone, potentially preventing the onset of CVD related to increased sympathetic activation. However, further studies are required to further understand how hypnosis and hypnotherapy affect the cardiovascular system through investigation of the neurophysiological components of the hypnotic state and of the mind-body relationship. Healthcare systems should embed mental health screening in patients at risk of developing CVD as part of the clinical pathway and consider the role that hypnosis and hypnotherapy may play in the management of CVD.

Background:Essential tremor (ET) and dystonic tremor (DT) are movement disorders that cause debilitating symptoms, significantly impacting daily activities and quality of life. A poor understanding of their pathophysiology, as well as the mediators of clinical outcomes following deep brain stimulation (DBS), highlights the need for biomarkers to accurately characterise and optimally treat patients. Objectives:We assessed the white matter microstructure of pathways implicated in the pathophysiology and therapeutic intervention in a retrospective cohort of patients with DT (n = 17) and ET (n = 19). We aimed to identity associations between white matter microstructure, upper limb tremor severity, and tremor improvement following DBS. Methods:A fixel-based analysis pipeline was implemented to investigate white matter microstructural metrics in the whole brain, cerebello-thalamic pathways and tracts connected to stimulation volumes following DBS. Associations with preoperative and postoperative severity were analysed within each disorder group and across combined disorder groups. Results:DBS led to significant improvements in both groups. No group differences in stimulation positions were identified. When white matter microstructural data was aligned according to the maximally affected upper limb, increased fiber density, and combined fiber density & cross-section of fixels in the left cerebellum were associated with greater tremor severity across DT and ET patients. White matter microstructure did not show associations with postoperative changes in cerebello-thalamic pathways, or tracts connected to stimulation volumes. Discussion:Diffusion changes of the cerebellum are associated with the severity of upper limb tremor and appear to overlap in essential or dystonic tremor disorders.

The spectrum, pathophysiology, and recovery trajectory of persistent post-COVID-19 cognitive deficits are unknown, limiting our ability to develop prevention and treatment strategies. We report the one-year cognitive, serum biomarker, and neuroimaging findings from a prospective, national longitudinal study of cognition in 351 COVID-19 patients who had required hospitalisation, compared to 2,927 normative matched controls. Cognitive deficits were global and associated with elevated brain injury markers and reduced anterior cingulate cortex volume one year after admission. The severity of the initial infective insult, post-acute psychiatric symptoms, and a history of encephalopathy were associated with greatest deficits. There was strong concordance between subjective and objective cognitive deficits. Treatment with corticosteroids during the acute phase appeared protective against cognitive deficits. Together, these findings support the hypothesis that brain injury in moderate to severe COVID-19 is immune-mediated, and should guide the development of therapeutic strategies.

The spectrum, pathophysiology and recovery trajectory of persistent post-COVID-19 cognitive deficits are unknown, limiting our ability to develop prevention and treatment strategies. We report the 1-year cognitive, serum biomarker and neuroimaging findings from a prospective, national study of cognition in 351 COVID-19 patients who required hospitalization, compared with 2,927 normative matched controls. Cognitive deficits were global, associated with elevated brain injury markers and reduced anterior cingulate cortex volume 1 year after COVID-19. Severity of the initial infective insult, postacute psychiatric symptoms and a history of encephalopathy were associated with the greatest deficits. There was strong concordance between subjective and objective cognitive deficits. Longitudinal follow-up in 106 patients demonstrated a trend toward recovery. Together, these findings support the hypothesis that brain injury in moderate to severe COVID-19 may be immune-mediated, and should guide the development of therapeutic strategies. A national prospective study of patients requiring hospitalization for COVID-19 demonstrates global cognitive deficits at 1 year, associated with elevated brain injury markers and reduced gray matter volume.

Central post stroke pain (CPSP) is a neuropathic pain condition prevalent in 8% to 35% of stroke patients. This systematic review and meta-analysis aimed to provide insight in the effectiveness of available pharmacological, physical, psychological, and neuromodulation intervention in reducing pain in CPSP patients (PROSPERO Registration: CRD42022371835). Secondary outcomes included mood, sleep, global impression of change, and physical responses. Data extraction included participant demographics, stroke aetiology, pain characteristics, pain reduction scores, and secondary outcome metrics. Forty two original studies were included with a total of 1451 participants. No studies providing psychological therapy to CPSP patients were identified. Twelve studies met requirements for a random-effects meta-analyses that found: pharmacological therapy to have a small effect on mean pain score (SMD = -0.36, 96.0% Confidence Interval [-0.68, -0.03], physical interventions did not show a significant effect (SMD = -0.55, [-1.28, 0.18]), and neuromodulation treatments had a moderate effect (SMD -0.64, [-1.08, -0.19]). Fourteen studies were included in proportional meta-analysis with pharmacological studies having a moderate effect (58.3% mean pain reduction, [-36.51, -80.15]), and neuromodulation studies a small effect (31.1% mean pain reduction, [-43.45, -18.76]). Sixteen studies were included in the narrative review, findings from which largely supported meta-analyses results. Duloxetine, Amitriptyline and repetitive Transcranial Magnetic Stimulation (rTMS) had the most robust evidence for their effectiveness in alleviating CPSP induced pain. Further multi-centre placebo-controlled research is needed to ascertain the effectiveness of physical therapies, such as acupuncture and virtual reality, and invasive and non-invasive neuromodulation treatments. Perspective This article presents a top-down and bottom-up overview of evidence for the effectiveness of different pharmacological, physical, and neuromodulation treatments of CPSP. This review could provide clinicians with a comprehensive understanding of the effectiveness and tolerability of different treatment types.