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Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics. The effects of different choices on preprocessing pipelines for functional connectomics remain unclear. Here, the authors systematically evaluate a multitude of pipelines on resting-state fMRI, revealing a number of optimal pipelines for functional brain network analysis.

Background The current study aims to systematically assess and compare the antidepressant outcomes of repetitive transcranial magnetic stimulation (rTMS) with the figure-of-eight (F8)-coil and deep transcranial magnetic stimulation (DTMS) with the H1-coil in studies matched on stimulation frequency in unipolar major depressive disorder (MDD). Methods Electronic search of Medline and PsycInfo identified 19 studies with stimulation frequency of 18–20 Hz using F8-coil ( k  = 8 randomised sham-controlled trials, RCTs, k  = 3 open-label; n  = 168 patients) or H1-coil ( k  = 1 RCT, k  = 7 open-label; n  = 200). Depression severity (the primary outcome) and response/remission rates (the secondary outcomes) were assessed at session 10. Results Effects pooled with random-effects meta-analysis showed a large reduction in depression severity, 29% response, and 15% remission rates after 10 sessions of active stimulation with either coil relative to baseline. Reduction in depression severity was greater in studies with younger patients using either coil. The comparison between coils showed a larger reduction in depression severity in H1-coil vs. F8-coil studies (independent of the study design or the concurrent pharmacotherapy) and a trend towards higher remission rates in F8-coil vs. H1-coils studies. These effects are based on a low volume of studies, are not controlled for placebo, and may not be clinically-relevant. The stimulation protocols differed systematically because stimulation was more focal but less intense (80–110% of the resting motor threshold, MT) in the F8-coil studies and less focal but more intense (120% MT) in the H1-coil studies. Two seizures occurred in the H1-coil studies relative to none in the F8-coil studies. Conclusion When matched on frequency, the higher-intensity and less focal stimulation with the H1-coil reduces depression more than the lower-intensity and more focal stimulation with the F8-coil. Head-to-head trials should compare the antidepressant outcomes of F8-coil and H1-coil to identify the most optimal stimulation protocols for acute and longer-lasting efficacy.

Engagement in a variety of lifestyle activities, such as intellectual stimulation, social interaction, and physical exercise, is thought to be a key contributor to cognitive reserve, helping the brain compensate for age-related or pathological changes. An open question is whether restrictions on lifestyle activities, even if relatively brief, might have detrimental effects on cognition. The COVID-19 pandemic led to unprecedented restrictions on the kinds of lifestyle activities that have been shown to be protective against age-related cognitive decline. In the present study, we captured changes in lifestyle and memory of older adults across the pandemic. Long-term memory was assessed using a task which allows for the estimation of both retrieval success and memory precision, the latter being particularly sensitive to age-related changes. Memory was assessed before the pandemic in person, and during the pandemic using an online version of the task. Experiment 1 first verified that younger adults’ performance did not significantly differ between testing environments, validating pre- and post-pandemic comparison in older adults. Experiment 2 then demonstrated that while substantial declines in lifestyle engagement were observed during the pandemic in older adults, there was no significant correlation between these lifestyle changes and memory performance overall. However, when modelling retrieval success, lifestyle effects varied with dementia risk, consistent with cognitive reserve theory, as well as varying with depression. These findings highlight how different memory features are impacted by factors such as lifestyle, and support the proposal that heightened dementia risk may increase susceptibility to the impact of lifestyle changes.

At this critical juncture in the development of NeuroAI, we outline challenges and training needs of junior researchers working across AI and neuroscience. We also provide advice and resources to help trainees plan their NeuroAI careers. New developing area of NeuroAI at the intersection of neuroscience and artificial intelligence has many open challenges, one of which is training the new generation of experts. In this Comment, the authors provide resources and outline training needs and recommendations for junior researchers working across artificial intelligence and neuroscience.

Background Cerebral small vessel disease (CSVD) is a common neurological condition that contributes to strokes, dementia, disability, and mortality worldwide. We conducted a systematic review and meta-analysis to investigate the use of neuroimaging CSVD markers in machine learning (ML) based diagnosis and prognosis of cognitive impairment and dementia, and identify both methodological changes over time and barriers to clinical translation. Methods Following the PRISMA guidelines, we systematically searched for original studies that used both neuroimaging CSVD markers and ML methods for diagnosing and prognosing neurodegenerative diseases (preregistration in PROSPERO: CRD42022366767). Each paper was independently reviewed by a pair of reviewers at all stages, with a third consulted to resolve conflicts. We meta-analysed the effectiveness of ML models to distinguish healthy controls from Alzheimer’s dementia and cognitive impairment, using area under the curve (AUC) as the performance metric. Results We identified 75 studies: 43 on diagnosis, 27 on prognosis, and 5 on both. Nearly 60% of studies were published in the past two years, reflecting a growing interest in using CSVD markers in ML-based diagnosis and prognosis of neurodegenerative diseases, especially Alzheimer’s dementia. This rising interest may be linked to the strong performance of such models: according to our meta-analysis, ML approaches using CSVD markers perform well in differentiating healthy controls from Alzheimer’s dementia (AUC 0.88 [95%-CI 0.85–0.92]) and cognitive impairment (AUC 0.84 [95%-CI 0.74–0.95]). However, the growing interest has not been matched by methodological rigour: only 16 studies met the criteria for inclusion in the meta-analysis due to inconsistent reporting, only five assessed the generalisability of their models on external datasets, and six lacked clear diagnostic criteria. Conclusions Interest in incorporating CSVD markers into ML models for neurodegenerative disease classification is on the rise, and their performance suggests that this is worth further exploration. Serious methodological issues, including inconsistent reporting, limited generalisability testing, and other potential biases, are unfortunately common and hinder further adoption. Our targeted recommendations provide a roadmap to accelerate the integration of ML into clinical practice.

IntroductionThe cerebellum has strong cortical and subcortical connectivity, but is rarely taken into account for clinical diagnosis in many neurodegenerative conditions, particularly in the absence of clinical ataxia. The current meta-analysis aims to assess patterns of cerebellar grey matter atrophy in seven neurodegenerative conditions (Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), multiple system atrophy (MSA), progressive supranuclear palsy (MSP)).MethodsWe carried out a systematic search in PubMed (any date: 14 July 2016) and a hand search of references from pertinent articles according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors were contacted to provide missing coordinate data. Peer-reviewed studies with direct comparison of patient and control groups, and availability of coordinate data of grey matter cerebellar atrophy in patients were included. These coordinates were used in an anatomical likelihood estimation meta-analysis.ResultsAcross 54 studies, clusters of cerebellar atrophy were found for AD, ALS, FTD, MSA, and PSP. Atrophy patterns were largely disease-specific, with overlap in certain areas of the cerebellar hemisphere, which showed marked atrophy in AD, ALS, FTD and PSP (Crus I/II), and MSA and PSP (lobules I–IV), respectively. Atrophy colocated with cerebellar areas implicated for motor (PSP, MSA) or cognitive symptoms (FTD, ALS, PSP) in the diseases.DiscussionOur findings suggest that cerebellar changes are largely disease-specific and correspond to cortical or subcortical changes in neurodegenerative conditions. High clinical variability in PD and HD samples may explain the absence of findings for consistent grey matter loss across studies. Our results have clinical implications for diagnosis and cerebellar neuroimaging referencing approaches.

Background Mnemonic discrimination (MD), the ability to distinguish between similar but distinct memories, relies heavily on the integrity of key medial temporal lobe (MTL) regions. Aging is associated with declines in MD, but evidence suggests that cognitive training may help mitigate this decline. While prior research has shown that such training can improve MD performance, little is known about the relationship between these behavioral improvements and structural changes in specific brain regions. This study investigates whether an 8‐week web‐based MD training program enhances MD in older adults and examines whether these gains are linked to volumetric changes in MTL areas, including the A36 perirhinal area, a critical region for object and scene memory. Method A total of 151 older adults (age M = 69.71 years, SD = 4.17) participated in an 8‐week web‐based MD training program. Participants were divided into three groups: object stimuli training (OG), scene stimuli training (SG), and an active control group (AC). A subset of OG (n = 33) and AC (n = 28) participants underwent pre‐ and post‐training 7T MRI scans (MP2RAGE 0.6 mm; T2w imaging: 0.4 x 0.4 x 1 mm along the hippocampal axis). The training required participants to differentiate between similar objects and scenes ('lures') and repeated items ('repeats’) in a progressively challenging MD task. Pre‐ and post‐training, participants completed a behavioral battery assessing MD and cognitive transfer effects. Volumetric changes in key MTL subregions, including the A36 perirhinal area, were assessed using the T2w ASHS algorithm for the MTL areas’ segmentations. Result Behavioral results showed significant gains in MD in the OG and SG groups compared to the control group. Notably, within the object training but not the control group, volumetric increases in the left A36 perirhinal area were specifically linked to MD performance gains for scenes (ie. near transfer to scenes) but not objects. This suggests a robust link between structural plasticity and MD behavioral improvement. Conclusion Our findings indicate that an 8‐week MD training program enhances memory performance in older adults and induces structural changes in the A36 perirhinal area, highlighting the potential for cognitive training to support brain health during aging.

The advent of remote and unsupervised digital cognitive assessment facilitates the use of frequent assessments only days apart to examine learning rates. Learning rates have been shown to differentiate patients with Mild Cognitive Impairment (MCI) and preclinical Alzheimer's disease (AD) from healthy individuals across a variety of cognitive measures. Here, we investigated whether impairments in learning rates in a mnemonic discrimination task were domain specific, as object and scene memory have been found to rely on anatomically distinct neural pathways that are differentially affected by AD. We examined learning rates over two weeks in the Mnemonic Discrimination Task for Objects and Scenes (MDT-OS) in N = 54 older adults (age = 67.1 ± 5.68) recruited through a memory clinic who were clinically cognitively unimpaired (CU, n = 44, n = 28, n = 6) or had MCI (n = 10, n = 3, n = 7). Five identical stimuli sets were completed at three-day intervals on participants' own smartphone or tablet. Using linear mixed modeling, we found significant cognitive status-by-time interactions indicating that the MCI group showed reduced learning compared to the CU group in the Scene condition, though not the Object condition. However, a cognitive status-by-time-by-condition model indicated no significant effect of condition. We found no significant Aβ status-by-time interaction in either condition. However, an Aβ status-by-time-by-condition model revealed diminished learning in the Scene condition compared to the Object condition for the Aβ+ group but not the Aβ- group. In a sub-group analysis including only CU individuals, this three-way interaction effect remained significant. The current results suggest that individuals with MCI show diminished learning rates in mnemonic discrimination of scenes, compared to CU individuals. We identified ceiling effects in the Object condition, which may have contributed to a null finding. Additionally, we found that Aβ positivity was associated with reduced learning of scenes but not objects, both in the overall sample and in individuals with no clinical indication of cognitive impairment. In conclusion, learning rates in the MDT-OS may be a sensitive marker of AD in individuals who do not yet meet the clinical threshold for cognitive impairment.

Background The advent of remote and unsupervised digital cognitive assessment facilitates the use of frequent assessments only days apart to examine learning rates. Learning rates have been shown to differentiate patients with Mild Cognitive Impairment (MCI) and preclinical Alzheimer’s disease (AD) from healthy individuals across a variety of cognitive measures. Here, we investigated whether impairments in learning rates in a mnemonic discrimination task were domain specific, as object and scene memory have been found to rely on anatomically distinct neural pathways that are differentially affected by AD. Methods We examined learning rates over two weeks in the Mnemonic Discrimination Task for Objects and Scenes (MDT‐OS) in N = 54 older adults (age = 67.1 ± 5.68) recruited through a memory clinic who were clinically cognitively unimpaired (CU, n = 44, nAβ– = 28, nAβ+ = 6) or had MCI (n = 10, nAβ– = 3, nAβ+ = 7). Five identical stimuli sets were completed at three‐day intervals on participants’ own smartphone or tablet. Results Using linear mixed modeling, we found significant cognitive status‐by‐time interactions indicating that the MCI group showed reduced learning compared to the CU group in the Scene condition, though not the Object condition. However, a cognitive status‐by‐time‐by‐condition model indicated no significant effect of condition. We found no significant Aβ status‐by‐time interaction in either condition. However, an Aβ status‐by‐time‐by‐condition model revealed diminished learning in the Scene condition compared to the Object condition for the Aβ+ group but not the Aβ– group. In a sub‐group analysis including only CU individuals, this three‐way interaction effect remained significant. Conclusion The current results suggest that individuals with MCI show diminished learning rates in mnemonic discrimination of scenes, compared to CU individuals. We identified ceiling effects in the Object condition, which may have contributed to a null finding. Additionally, we found that Aβ positivity was associated with reduced learning of scenes but not objects, both in the overall sample and in individuals with no clinical indication of cognitive impairment. In conclusion, learning rates in the MDT‐OS may be a sensitive marker of AD in individuals who do not yet meet the clinical threshold for cognitive impairment.

Mnemonic discrimination (MD), the ability to distinguish between similar but distinct memories, relies heavily on the integrity of key medial temporal lobe (MTL) regions. Aging is associated with declines in MD, but evidence suggests that cognitive training may help mitigate this decline. While prior research has shown that such training can improve MD performance, little is known about the relationship between these behavioral improvements and structural changes in specific brain regions. This study investigates whether an 8-week web-based MD training program enhances MD in older adults and examines whether these gains are linked to volumetric changes in MTL areas, including the A36 perirhinal area, a critical region for object and scene memory. A total of 151 older adults (age M = 69.71 years, SD = 4.17) participated in an 8-week web-based MD training program. Participants were divided into three groups: object stimuli training (OG), scene stimuli training (SG), and an active control group (AC). A subset of OG (n = 33) and AC (n = 28) participants underwent pre- and post-training 7T MRI scans (MP2RAGE 0.6 mm; T2w imaging: 0.4 x 0.4 x 1 mm along the hippocampal axis). The training required participants to differentiate between similar objects and scenes ('lures') and repeated items ('repeats') in a progressively challenging MD task. Pre- and post-training, participants completed a behavioral battery assessing MD and cognitive transfer effects. Volumetric changes in key MTL subregions, including the A36 perirhinal area, were assessed using the T2w ASHS algorithm for the MTL areas' segmentations. Behavioral results showed significant gains in MD in the OG and SG groups compared to the control group. Notably, within the object training but not the control group, volumetric increases in the left A36 perirhinal area were specifically linked to MD performance gains for scenes (ie. near transfer to scenes) but not objects. This suggests a robust link between structural plasticity and MD behavioral improvement. Our findings indicate that an 8-week MD training program enhances memory performance in older adults and induces structural changes in the A36 perirhinal area, highlighting the potential for cognitive training to support brain health during aging.