Specific cortical and subcortical grey matter regions are associated with insomnia severity

BACKGROUND: There is an increasing awareness that sleep disturbances are a risk factor for dementia. Prior case-control studies suggested that brain grey matter (GM) changes involving cortical (i.e, prefrontal areas) and subcortical structures (i.e, putamen, thalamus) could be associated with insomnia status. However, it remains unclear whether there is a gradient association between these regions and the severity of insomnia in older adults who could be at risk for dementia. Since depressive symptoms and sleep apnea can both feature insomnia-related factors, can impact brain health and are frequently present in older populations, it is important to include them when studying insomnia. Therefore, our goal was to investigate GM changes associated with insomnia severity in a cohort of healthy older adults, taking into account the potential effect of depression and sleep apnea as well. We hypothesized that insomnia severity is correlated with 1) cortical regions responsible for regulation of sleep and emotion, such as the orbitofrontal cortex and, 2) subcortical regions, such as the putamen. METHODS: 120 healthy subjects (age 74.8±5.7 years old, 55.7% female) were recruited from the Hillblom Healthy Aging Network at the Memory and Aging Center, UCSF. All participants were determined to be cognitively healthy following a neurological evaluation, neuropsychological assessment and informant interview. Participants had a 3T brain MRI and completed the Insomnia Severity Index (ISI), Geriatric Depression Scale (GDS) and Berlin Sleep Questionnaire (BA) to assess sleep apnea. Cortical thickness (CTh) and subcortical volumes were obtained by the CAT12 toolbox within SPM12. We studied the correlation of CTh and subcortical volumes with ISI using multiple regressions adjusted by age, sex, handedness and MRI scan type. Additional models adjusting by GDS and BA were also performed. RESULTS: ISI and GDS were predominantly mild (4.9±4.2 and 2.5±2.9, respectively) and BA was mostly low risk (80%). Higher ISI correlated with lower CTh of the right orbitofrontal, right superior and caudal middle frontal areas, right temporo-parietal junction and left anterior cingulate cortex (p<0.001, uncorrected FWE). When adjusting by GDS, right ventral orbitofrontal and temporo-parietal junction remained significant, and left insula became significant (p<0.001, uncorrected FWE). Conversely, BA showed no effect. The results were no longer significant following FWE multiple comparisons. Regarding subcortical areas, higher putamen volumes were associated with higher ISI (p<0.01). CONCLUSIONS: Our findings highlight a relationship between insomnia severity and brain health, even with relatively mild insomnia, and independent of depression and likelihood of sleep apnea. The results extend the previous literature showing the association of specific GM areas (i.e, orbitofrontal, insular and temporo-parietal junction) not just with the presence of insomnia, but across the spectrum of severity itself. Moreover, our results suggest subcortical structures (i.e., putamen) are involved as well. Longitudinal studies are needed to clarify how these insomnia-related brain changes in healthy subjects align with an increased risk of dementia

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.© 2023. The Author(s)

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC

Contribution of CSF biomarkers to early-onset Alzheimer's disease and frontotemporal dementia neuroimaging signatures

Prior studies have described distinct patterns of brain gray matter and white matter alterations in Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), as well as differences in their cerebrospinal fluid (CSF) biomarkers profiles. We aim to investigate the relationship between early‐onset AD (EOAD) and FTLD structural alterations and CSF biomarker levels. We included 138 subjects (64 EOAD, 26 FTLD, and 48 controls), all of them with a 3T MRI brain scan and CSF biomarkers available (the 42 amino acid‐long form of the amyloid‐beta protein [Aβ42], total‐tau protein [T‐tau], neurofilament light chain [NfL], neurogranin [Ng], and 14‐3‐3 levels). We used FreeSurfer and FSL to obtain cortical thickness (CTh) and fraction anisotropy (FA) maps. We studied group differences in CTh and FA and described the “AD signature” and “FTLD signature.” We tested multiple regression models to find which CSF‐biomarkers better explained each disease neuroimaging signature. CTh and FA maps corresponding to the AD and FTLD signatures were in accordance with previous literature. Multiple regression analyses showed that the biomarkers that better explained CTh values within the AD signature were Aβ and 14‐3‐3; whereas NfL and 14‐3‐3 levels explained CTh values within the FTLD signature. Similarly, NfL levels explained FA values in the FTLD signature. Ng levels were not predictive in any of the models. Biochemical markers contribute differently to structural (CTh and FA) changes typical of AD and FTLD

Synaptic, axonal damage and inflammatory cerebrospinal fluid biomarkers in neurodegenerative dementias

INTRODUCTION: Synaptic damage, axonal neurodegeneration, and neuroinflammation are common features in Alzheimer's disease (AD), frontotemporal dementia (FTD), and Creutzfeldt-Jakob disease (CJD). METHODS: Unicentric cohort of 353 participants included healthy control (HC) subjects, AD continuum stages, genetic AD and FTD, and FTD and CJD. We measured cerebrospinal fluid neurofilament light (NF-L), neurogranin (Ng), 14-3-3, and YKL-40 proteins. RESULTS: Biomarkers showed differences in HC subjects versus AD, FTD, and CJD. Disease groups differed between them except AD versus FTD for YKL-40. Only NF-L differed between all stages within the AD continuum. AD and FTD symptomatic mutation carriers presented differences with respect to HC subjects. Applying the AT(N) system, 96% subjects were positive for neurodegeneration if 14-3-3 was used, 94% if NF-L was used, 62% if Ng was used, and 53% if YKL-40 was used. DISCUSSION: Biomarkers of synapse and neurodegeneration differentiate HC subjects from neurodegenerative dementias and between AD, FTD, and CJD. NF-L and 14-3-3 performed similar to total tau when AT(N) system was applied

Cortical microstructure in primary progressive aphasia: a multicenter study

Cortical mean diffusivity is a novel imaging metric sensitive to early changes in neurodegenerative syndromes. Higher cortical mean diffusivity values reflect microstructural disorganization and have been proposed as a sensitive biomarker that might antedate macroscopic cortical changes. We aimed to test the hypothesis that cortical mean diffusivity is more sensitive than cortical thickness to detect cortical changes in primary progressive aphasia (PPA).In this multicenter, case-control study, we recruited 120 patients with PPA (52 non-fluent, 31 semantic, and 32 logopenic variants; and 5 GRN-related PPA) as well as 89 controls from three centers. The 3-Tesla MRI protocol included structural and diffusion-weighted sequences. Disease severity was assessed with the Clinical Dementia Rating scale. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach.The comparison between each PPA variant and controls revealed cortical mean diffusivity increases and cortical thinning in overlapping regions, reflecting the canonical loci of neurodegeneration of each variant. Importantly, cortical mean diffusivity increases also expanded to other PPA-related areas and correlated with disease severity in all PPA groups. Cortical mean diffusivity was also increased in patients with very mild PPA when only minimal cortical thinning was observed and showed a good correlation with measures of disease severity.Cortical mean diffusivity shows promise as a sensitive biomarker for the study of the neurodegeneration-related microstructural changes in PPA.© 2022. The Author(s)

Priorities for research on neuromodulatory subcortical systems in Alzheimer's disease: Position paper from the NSS PIA of ISTAART

The neuromodulatory subcortical system (NSS) nuclei are critical hubs for survival, hedonic tone, and homeostasis. Tau-associated NSS degeneration occurs early in Alzheimer's disease (AD) pathogenesis, long before the emergence of pathognomonic memory dysfunction and cortical lesions. Accumulating evidence supports the role of NSS dysfunction and degeneration in the behavioral and neuropsychiatric manifestations featured early in AD. Experimental studies even suggest that AD-associated NSS degeneration drives brain neuroinflammatory status and contributes to disease progression, including the exacerbation of cortical lesions. Given the important pathophysiologic and etiologic roles that involve the NSS in early AD stages, there is an urgent need to expand our understanding of the mechanisms underlying NSS vulnerability and more precisely detail the clinical progression of NSS changes in AD. Here, the NSS Professional Interest Area of the International Society to Advance Alzheimer's Research and Treatment highlights knowledge gaps about NSS within AD and provides recommendations for priorities specific to clinical research, biomarker development, modeling, and intervention. HIGHLIGHTS: Neuromodulatory nuclei degenerate in early Alzheimer's disease pathological stages. Alzheimer's pathophysiology is exacerbated by neuromodulatory nuclei degeneration. Neuromodulatory nuclei degeneration drives neuropsychiatric symptoms in dementia. Biomarkers of neuromodulatory integrity would be value-creating for dementia care. Neuromodulatory nuclei present strategic prospects for disease-modifying therapies