Resting-state fMRI (rs-fMRI) is a widely used technique to investigate the residual brain functions of patients with Disorders of Consciousness (DoC). Nonetheless, it is unclear how the networks that are more associated with primary functions, such as the sensory–motor, medial/lateral visual and auditory networks, contribute to clinical assessment. In this study, we examined the rs-fMRI lower-order networks alongside their structural MRI data to clarify the corresponding association with clinical assessment. We studied 109 chronic patients with DoC and emerged from DoC with structural MRI and rs-fMRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS) and 10 with severe disability. rs-fMRI data were analyzed with independent component analyses and seed-based analyses, in relation to structural MRI and clinical data. The results showed that VS/UWS had fewer networks than MCS patients and the rs-fMRI activity in each network was decreased. Visual networks were correlated to the clinical status, and in cases where no clinical response occurred, rs-fMRI indicated distinctive networks conveying information in a similar way to other techniques. The information provided by single networks was limited, whereas the four networks together yielded better classification results, particularly when the model included rs-fMRI and structural MRI data (AUC = 0.80). Both quantitative and qualitative rs-fMRI analyses yielded converging results; vascular etiology might confound the results, and disease duration generally reduced the number of networks observed. The lower-order rs-fMRI networks could be used clinically to support and corroborate visual function assessments in DoC.

C9orf72 mutation carriers with different neurological phenotypes show cortical and subcortical atrophy in multiple different brain regions, even in pre-symptomatic phases. Despite there is a substantial amount of knowledge, small sample sizes, clinical heterogeneity, as well as different choices of image analysis may hide anatomical abnormalities that are unique to amyotrophic lateral sclerosis (ALS) patients with this genotype or that are indicative of the C9orf72-specific trait overlain in fronto-temporal dementia patients. Brain structural and resting state functional magnetic imaging was obtained in 24 C9orf72 positive (ALSC9+) ALS patients paired for burden disease with 24 C9orf72 negative (ALSC9-) ALS patients. A comprehensive structural evaluation of cortical thickness and subcortical volumes between ALSC9+ and ALSC9- patients was performed while a region of interest (ROI)-ROI analysis of functional connectivity was implemented to assess functional alterations among abnormal cortical and subcortical regions. Results were corrected for multiple comparisons. Compared to ALSC9- patients, ALSC9+ patients exhibited extensive disease-specific patterns of thalamo-cortico-striatal atrophy, supported by functional alterations of the identified abnormal regions. Cortical thinning was most pronounced in posterior areas and extended to frontal regions. Bilateral atrophy of the mediodorsal and pulvinar nuclei was observed, emphasizing a focal rather than global thalamus atrophy. Volume loss in a large portion of bilateral caudate and left putamen was reported. The marked reduction of functional connectivity observed between the left posterior thalamus and almost all the atrophic cortical regions support the central role of the thalamus in the pathogenic mechanism of C9orf72-mediated disease. These findings constitute a coherent and robust picture of ALS patients with C9orf72-mediated disease, unveiling a specific structural and functional characterization of thalamo-cortico-striatal circuit alteration. Our study introduces new evidence in the characterization of the pathogenic mechanisms of C9orf72 mutation.

Background and purpose: Thalamic alterations have been reported as a major feature in presymptomatic and symptomatic patients carrying the C9orf72 mutation across the frontotemporal dementia–amyotrophic lateral sclerosis (ALS) spectrum. Specifically, the pulvinar, a high‐order thalamic nucleus and timekeeper for large‐scale cortical networks, has been hypothesized to be involved in C9orf72‐related neurodegenerative diseases. We investigated whether pulvinar volume can be useful for differential diagnosis in ALS C9orf72 mutation carriers and noncarriers and how underlying functional connectivity changes affect this region.Methods: We studied 19 ALS C9orf72 mutation carriers (ALSC9+) accurately matched with wild‐type ALS (ALSC9−) and ALS mimic (ALSmimic) patients using structural and resting‐state functional magnetic resonance imaging data. Pulvinar volume was computed using automatic segmentation. Seed‐to‐voxel functional connectivity analyses were performed using seeds from a pulvinar functional parcellation.Results: Pulvinar structural integrity had high discriminative values for ALSC9+ patients compared to ALSmimic (area under the curve [AUC] = 0.86) and ALSC9− (AUC = 0.77) patients, yielding a volume cutpoint of approximately 0.23%. Compared to ALSmimic, ALSC9− showed increased anterior, inferior, and lateral pulvinar connections with bilateral occipital–temporal–parietal regions, whereas ALSC9+ showed no differences. ALSC9+ patients when compared to ALSC9− patients showed reduced pulvinar–occipital connectivity for anterior and inferior pulvinar seeds.Conclusions: Pulvinar volume could be a differential biomarker closely related to the C9orf72 mutation. A pulvinar–cortical circuit dysfunction might play a critical role in disease progression and development, in both the genetic phenotype and ALS wild‐type patients.

Background/Objectives: Cognitive decline and Alzheimer’s disease (AD) pose a major challenge for the ageing population, with impaired synaptic plasticity playing a central role in their pathophysiology. This article explores the hypothesis that cortico–cortical paired associative stimulation (ccPAS), a non-invasive brain stimulation technique, can restore synaptic function by targeting impaired spike-timing-dependent plasticity (STDP), a key mechanism disrupted in AD. Methods: We reviewed existing studies investigating the effects of ccPAS on neuroplasticity in both ageing and AD populations. Results: Findings suggest age-specific effects, with ccPAS improving motor performance in young adults but showing limited efficacy in older adults, likely due to age-related declines in synaptic plasticity and cortical excitability. In AD, ccPAS studies reveal significant impairments in long-term potentiation (LTP)-like plasticity, while long-term depression (LTD)-like mechanisms appear relatively preserved, emphasising the need for targeted neuromodulation approaches. Conclusions: Despite promising preliminary results, evidence remains limited and largely focused on motor function, with the impact of ccPAS on cognitive domains still underexplored. To bridge this gap, future research should focus on larger and more diverse cohorts to optimise ccPAS protocols for ageing and AD populations and investigate its potential for enhancing cognitive function. By refining stimulation parameters and integrating neuroimageing-based personalisation strategies, ccPAS may represent a novel therapeutic approach for mitigating neuroplasticity deficits in ageing and neurodegenerative conditions.

Background/Objectives: The surgical management of trigeminal schwannomas (TSs) has evolved considerably, with increasing interest in minimally invasive approaches. We performed a meta-regression analysis to characterise temporal trends in surgical strategies for TS and to explore factors influencing outcomes. Methods: This systematic review and meta-regression followed the PRISMA 2020 guidelines. Comparative studies published in English reporting surgical treatment of TS were included. Outcomes assessed were the extent of resection (EOR), improvement or worsening of trigeminal symptoms, and postoperative complications. Meta-analyses of pooled frequencies were performed, and meta-regression analyses evaluated associations between surgical approach, tumour localization, year of publication, and outcomes. Surgical approaches were categorized as microsurgical antero-lateral (M-AL-Apr), retrosigmoid (RSA), endoscopic endonasal (EEA), and endoscopic transorbital (ETOA). Tumour localization was stratified using the Samii classification. Results: Fifteen studies (583 surgeries) were included. Endoscopic approaches accounted for 20.1% of cases, with increasing use over time (β = 0.12—p < 0.001), largely driven by transorbital access for Samii type A and C tumours. The use of M-AL-Apr declined. The pooled gross-total resection (GTR) rate was 73% (I² = 78.8%). The stratified meta-regression identified a temporal decrease in GTR for Samii type C tumours alone, while resection rates for types A, B, and D remained stable, likely reflecting the increasing proportion of anatomically complex cases in recent series Trigeminal impairment improved postoperatively in 17% (I² = 84.5%), while worsening of trigeminal symptoms was rare (β = 0.07%—I² = 0%). Complication rates were 11.6% (I² = 32.7%) but with a temporal increase (β = 0.041, p = 0.047). Tumour type was the dominant predictor of EOR, functional outcomes, and complications. Conclusions: Surgical management of TS has evolved towards minimally invasive techniques, particularly endoscopic routes, reflecting advances in technology and a focus on functional preservation. Tumour anatomy remains the key determinant of surgical outcomes, highlighting the importance of tailored, anatomy-driven surgical planning.

Amyotrophic Lateral Sclerosis (ALS) is an incurable deadly motor neuron disease that causes the gradual deterioration of nerve cells in the spinal cord and brain. It impacts voluntary limb control and can result in breathing impairment. ALS diagnosis is often challenging due to its symptoms overlapping with other medical conditions and many tests must be performed to rule out other conditions, as easily identifiable biomarkers are still lacking. In this study, we explore T1-weighted (T1w) brain Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging approach which has shown to be a reliable biomarker in many medical fields. Nonetheless, current literature on ALS diagnosis fails to retrieve evidence on how to identify biomarkers from T1w MRI. In this paper, we leverage Artificial Intelligence (AI) methods to unveil the unexplored potential of T1w brain MRI for distinguishing ALS patients from those who have similar symptoms but different diseases ( mimicking ). We consider a retrospective single-center dataset of brain T1-weighted MRIs collected from 2010 to 2018 recruited from the Piemonte and Valle d’Aosta ALS register (PARALS). The collection includes 548 patients with ALS and 106 with mimicking diseases. Our goal is to develop and validate a ML diagnostic model based exclusively on T1w MRI distinguishing the two classes. First, we extract a set of radiomic features and two sets of Deep Learning (DL)-based features from MRI scans. Then, using each representation, we train 8 binary classifiers. The best results were obtained by combining DL-based features with SVM classifier, reaching an F1-score of 0.91, and a Precision of 0.88, a Recall of 0.94, and an AUC of 0.7 considering the ALS group as the positive class in the testing set.