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Despite advances towards understanding the molecular pathophysiology of the neurodegenerative dementias, the mechanisms linking molecular changes to neuropathology and neuropathological changes to clinical symptoms remain largely obscure. Connectivity is a distinctive feature of the brain and the integrity of functional network dynamics is crucial for normal functioning. A better understanding of network disruption in the neurodegenerative dementias might help bridge the gap between molecular changes, pathological changes, and symptoms. Recent findings on functional network disruption as assessed with resting-state or intrinsic connectivity functional MRI and electroencephalography and magnetoencephalography have shown distinct patterns of network disruption across the major neurodegenerative diseases. These network abnormalities are somewhat specific to the clinical syndromes and, in Alzheimer's disease and frontotemporal dementia, network disruption tracks the pattern of pathological changes. These findings might have practical implications for diagnostic accuracy, allowing earlier detection of neurodegenerative diseases even at the presymptomatic stage, and tracking of disease progression.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regression, support vector machines and Gaussian process models. With the advance of deep learning technology, the use of deep neural networks has also been proposed. In this study, we assessed normative models based on deep autoencoders using structural neuroimaging data from patients with Alzheimer’s disease (n = 206) and mild cognitive impairment (n = 354). We first trained the autoencoder on an independent dataset (UK Biobank dataset) with 11,034 healthy controls. Then, we estimated how each patient deviated from this norm and established which brain regions were associated to this deviation. Finally, we compared the performance of our normative model against traditional classifiers. As expected, we found that patients exhibited deviations according to the severity of their clinical condition. The model identified medial temporal regions, including the hippocampus, and the ventricular system as critical regions for the calculation of the deviation score. Overall, the normative model had comparable cross-cohort generalizability to traditional classifiers. To promote open science, we are making all scripts and the trained models available to the wider research community.

Background Increasing evidence links the gut microbiota (GM) to Alzheimer’s disease (AD) but the mechanisms through which gut bacteria influence the brain are still unclear. This study tests the hypothesis that GM and mediators of the microbiota-gut-brain axis (MGBA) are associated with the amyloid cascade in sporadic AD. Methods We included 34 patients with cognitive impairment due to AD (CI-AD), 37 patients with cognitive impairment not due to AD (CI-NAD), and 13 cognitively unimpaired persons (CU). We studied the following systems: (1) fecal GM, with 16S rRNA sequencing; (2) a panel of putative MGBA mediators in the blood including immune and endothelial markers as bacterial products (i.e., lipopolysaccharide, LPS), cell adhesion molecules (CAMs) indicative of endothelial dysfunction (VCAM-1, PECAM-1), vascular changes (P-, E-Selectin), and upregulated after infections (NCAM, ICAM-1), as well as pro- (IL1β, IL6, TNFα, IL18) and anti- (IL10) inflammatory cytokines; (3) the amyloid cascade with amyloid PET, plasma phosphorylated tau (pTau-181, for tau pathology), neurofilament light chain (NfL, for neurodegeneration), and global cognition measured using MMSE and ADAScog. We performed 3-group comparisons of markers in the 3 systems and calculated correlation matrices for the pooled group of CI-AD and CU as well as CI-NAD and CU. Patterns of associations based on Spearman’s rho were used to validate the study hypothesis. Results CI-AD were characterized by (1) higher abundance of Clostridia_UCG-014 and decreased abundance of Moryella and Blautia ( p  < .04); (2) elevated levels of LPS ( p  < .03), upregulation of CAMs, Il1β, IL6, and TNFα, and downregulation of IL10 ( p  < .05); (3) increased brain amyloid, plasma pTau-181, and NfL ( p  < 0.004) compared with the other groups. CI-NAD showed (1) higher abundance of [Eubacterium] coprostanoligenes group and Collinsella and decreased abundance of Lachnospiraceae_ND3007_group , [Ruminococcus]_gnavus_group and Oscillibacter ( p  < .03); (2) upregulation of PECAM-1 and TNFα ( p  < .03); (4) increased plasma levels of NfL ( p  < .02) compared with CU. Different GM genera were associated with immune and endothelial markers in both CI-NAD and CI-AD but these mediators were widely related to amyloid cascade markers only in CI-AD. Conclusions Specific bacterial genera are associated with immune and endothelial MGBA mediators, and these are associated with amyloid cascade markers in sporadic AD. The physiological mechanisms linking the GM to the amyloid cascade should be further investigated to elucidate their potential therapeutic implications.

Frontotemporal dementia is a highly heritable neurodegenerative disorder. In about a third of patients, the disease is caused by autosomal dominant genetic mutations usually in one of three genes: progranulin (GRN), microtubule-associated protein tau (MAPT), or chromosome 9 open reading frame 72 (C9orf72). Findings from studies of other genetic dementias have shown neuroimaging and cognitive changes before symptoms onset, and we aimed to identify whether such changes could be shown in frontotemporal dementia. We recruited participants to this multicentre study who either were known carriers of a pathogenic mutation in GRN, MAPT, or C9orf72, or were at risk of carrying a mutation because a first-degree relative was a known symptomatic carrier. We calculated time to expected onset as the difference between age at assessment and mean age at onset within the family. Participants underwent a standardised clinical assessment and neuropsychological battery. We did MRI and generated cortical and subcortical volumes using a parcellation of the volumetric T1-weighted scan. We used linear mixed-effects models to examine whether the association of neuropsychology and imaging measures with time to expected onset of symptoms differed between mutation carriers and non-carriers. Between Jan 30, 2012, and Sept 15, 2013, we recruited participants from 11 research sites in the UK, Italy, the Netherlands, Sweden, and Canada. We analysed data from 220 participants: 118 mutation carriers (40 symptomatic and 78 asymptomatic) and 102 non-carriers. For neuropsychology measures, we noted the earliest significant differences between mutation carriers and non-carriers 5 years before expected onset, when differences were significant for all measures except for tests of immediate recall and verbal fluency. We noted the largest Z score differences between carriers and non-carriers 5 years before expected onset in tests of naming (Boston Naming Test −0·7; SE 0·3) and executive function (Trail Making Test Part B, Digit Span backwards, and Digit Symbol Task, all −0·5, SE 0·2). For imaging measures, we noted differences earliest for the insula (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume was 0·80% in mutation carriers and 0·84% in non-carriers; difference −0·04, SE 0·02) followed by the temporal lobe (at 10 years before expected symptom onset, mean volume as a percentage of total intracranial volume 8·1% in mutation carriers and 8·3% in non-carriers; difference −0·2, SE 0·1). Structural imaging and cognitive changes can be identified 5–10 years before expected onset of symptoms in asymptomatic adults at risk of genetic frontotemporal dementia. These findings could help to define biomarkers that can stage presymptomatic disease and track disease progression, which will be important for future therapeutic trials. Centres of Excellence in Neurodegeneration.

A growing number of studies is using fMRI-based connectivity to guide transcranial magnetic stimulation (TMS) target identification in both normal and clinical populations. TMS has gained increasing attention as a potential therapeutic strategy also in Alzheimer’s disease (AD), but an endorsed target localization strategy in this population is still lacking. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability. Single-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality. The distance between individual fMRI-derived coordinates and traditionally defined targets was significant for a supposed TMS focality of 12 mm and in some cases up to 20 mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets. The proposed network-based fMRI-guided TMS approach, while accounting for inter-individual functional variability, allows to target core AD networks, and might thus represent a step toward tailored TMS interventions for AD.

Borderline Personality Disorder (BPD) is a severe mental disorder marked by emotional dysregulation. Estimates show that 73% of patients with BPD will have, on average, three suicide attempts in their lifetime, with up to 10% of cases resulting in death. Reliable tools to identify risk factors associated with suicide are lacking. Artificial Intelligence (AI) could fill this gap, supporting the development of effective intervention strategies. This pilot study provides preliminary evidence that a multimodal signature could differentiate suicide attempts in individuals with BPD, paving the way to prospective cohort validation and clinical applications. We developed DRAMA-BPD (Detecting Retrospective suicide Attempts with Machine learning Approaches in Borderline Personality Disorder), an explainable, multimodal, Machine Learning (ML) model based on an ensemble classifier of lifetime suicide attempters among people with BPD. DRAMA-BPD was trained on the sociodemographic, clinical, and MRI data of 104 individuals with BPD recruited from two cohorts. Processing techniques adopted included feature extraction. SHapley Additive exPlanations (SHAP) was used to assess model interpretability. DRAMA-BPD achieved a balanced accuracy of 0.68, sensitivity of 0.58, specificity of 0.77, and AUC of 0.68. SHAP analysis identified cortical volumes and thickness from T1-weighted images and Symptoms Checklist 90 Revised (SCL-90-R) as the main contributors to classification.

The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session. Significant MRI site and vendor effects (p ​< ​.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p ​< ​1.39E-36). In particular, volumes larger than 200 ​mm3 (for amygdalar nuclei) and 300 ​mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε ​< ​5% and DICE ​> ​0.80). Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials. •Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.

Background Auraptene (AUR) and naringenin (NAR) are citrus-derived phytochemicals that influence several biological mechanisms associated with cognitive decline, including neuronal damage, oxidative stress and inflammation. Clinical evidence of the efficacy of a nutraceutical with the potential to enhance cognitive function in cohorts at risk of cognitive decline would be of great value from a preventive perspective. The primary aim of this study is to determine the cognitive effects of a 36-week treatment with citrus peel extract standardized in levels of AUR and NAR in older adults experiencing subjective cognitive decline (SCD). The secondary aim is to determine the effects of these phytochemicals on blood-based biomarkers indicative of neuronal damage, oxidative stress, and inflammation. Methods Eighty older persons with SCD will be recruited and randomly assigned to receive the active treatment (400 mg of citrus peel extract containing 0.1 mg of AUR and 3 mg of NAR) or the placebo at a 1:1 ratio for 36 weeks. The primary endpoint is a change in the Repeatable Battery for the Assessment of Neuropsychological Status score from baseline to weeks 18 and 36. Other cognitive outcomes will include changes in verbal and nonverbal memory, attention, executive and visuospatial functions. Blood samples will be collected from a consecutive subsample of 60 participants. The secondary endpoint is a change in interleukin-8 levels over the 36-week period. Other biological outcomes include changes in markers of neuronal damage, oxidative stress, and pro- and anti-inflammatory cytokines. Conclusion This study will evaluate whether an intervention with citrus peel extract standardized in levels of AUR and NAR has cognitive and biological effects in older adults with SCD, facilitating the establishment of nutrition intervention in people at risk of cognitive decline. Trial registration The trial is registered with the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT04744922 on February 9 th , 2021 ( https://www.clinicaltrials.gov/ct2/show/NCT04744922 ).

Background Developing interventions for older adults with subjective cognitive decline (SCD) has the potential to prevent dementia in this at-risk group. Preclinical models indicate that Citrus -derived phytochemicals could benefit cognition and inflammatory processes, but results from clinical trials are still preliminary. The aim of this study is to determine the effects of long-term supplementation with Citrus peel extract on cognitive performance and inflammation in individuals with SCD. Methods Eighty participants were randomly assigned to active treatment (400 mg of Citrus peel extract containing 3.0 mg of naringenin and 0.1 mg of auraptene) or placebo at 1:1 ratio for 36 weeks. The primary endpoint was the change in the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) total score across the 36-week trial period. Other cognitive outcomes included tests and scales evaluating verbal memory, attention, executive and visuospatial functions, and memory concerns. The secondary endpoint was the change of interleukin-8 (IL-8) levels over the 36-week trial period in a subsample of 60 consecutive participants. An Intention-to-treat approach with generalized linear mixed models was used for data analysis. Results The RBANS total score showed significant improvement in both Citrus peel extract and placebo groups at 36 weeks ( p for time < .001, d  = 0.36, p time x treatment = .910). Significant time effects were also found in cognitive domains of short- and long-term verbal memory ( p  < .001) and scales of subjective memory ( p  < .01), with no significant time x treatment interaction. The largest effect sizes were observed in verbal memory in the placebo group ( d  = 0.69 in short-term, and d  = 0.78 in long-term verbal memory). Increased IL-8 levels were found at 36-week follow-up in both Citrus peel extract and placebo groups ( p for time = .010, d  = 0.21, p time x treatment = .772). Adverse events were balanced between groups. Conclusions In this randomized clinical trial, long-term Citrus peel extract supplementation did not show cognitive benefits over placebo in participants with SCD, possibly due to high placebo response. These findings might have specific implications for designing future nutraceutical trials in individuals experiencing SCD. Trial registration The trial has been registered at the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT04744922 on February 9th, 2021 ( https://www.clinicaltrials.gov/ct2/show/NCT04744922 ).

Background The development of effective strategies to maintain good mental health of older adults is a public health priority. Mindfulness-based interventions have the potential to improve psychological well-being and cognitive functions of older adults, but little is known about the effect of such interventions when delivered through internet. During the COVID-19 pandemic we evaluated short- and long-term cognitive, psychological, and physiological effects of a mindfulness-based intervention (MBI) delivered via web-based videoconference in healthy older adults. Methods Fifty older adults participated in an 8-week MBI, which comprised structured 2-h weekly group sessions. A comprehensive evaluation encompassing cognitive (verbal memory, attention and processing speed, executive functions) and psychological assessments (depression and anxiety symptoms, mindfulness, worries, emotion regulation strategies, well-being, interoceptive awareness and sleep) was conducted. Additionally, electroencephalography (EEG) data were recorded before and after the MBI and at the 6-month follow-up (T6). Data were analyzed using an intention-to-treat approach, using linear mixed models adjusted for age. The effect size for time was computed as omega squared. Results We observed significant improvements from pre-MBI to post-MBI and at the T6 across several measures. These improvements were notable in the areas of verbal memory (California Verbal Learning Test, p  ≤ .007), attention and executive functions (Trail Making Test A and BA, p  < .050), interoceptive awareness (Multidimensional Assessment of Interoceptive Awareness, p  = .0002 for self-regulation and p  < .05 for noticing, body listening, and trusting dimensions), and rumination (Heidelberg Form for Emotion Regulation Strategies, p  = .018). These changes were associated with low to medium effect size. Moreover, we observed significant changes in EEG patterns, with a decrease in alpha1 ( p  = .004) and an increase in alpha2 ( p  < .0001) from pre-MBI to T6. Notably, improvements in TMTBA and rumination were correlated with the decrease in alpha1 ( p  < .050), while improvements in TMTA were linked to the increase in alpha2 ( p  = .025). Conclusions The results of our study show that a web-based MBI in older adults leads to improvements in cognitive and psychological measures, with associated modulations in specific brain rhythms. While these findings are promising, further controlled studies are required to validate these preliminary results. Trial registration The trial has been registered with the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT05941143 on July 12, 2023.