Gray matter volume and estimated brain age gap are not linked with sleep‐disordered breathing

Abstract

Alzheimer's disease (AD) and sleep‐disordered breathing (SDB) are prevalent conditions with a rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self‐reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HCs). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self‐reported history of SDB, and matched across diagnoses for age, sex and presence of the Apolipoprotein E4 allele, from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Gray‐matter volume was measured using voxel‐wise morphometry and group differences in terms of SDB, cognitive status, and their interaction were assessed. Further, using an age‐prediction model fitted on gray‐matter data of external datasets, we predicted study participants' age from their structural images. Cognitive decline and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. Brains age was well predicted from the morphological data in HCs and, as expected, elevated in MCI and particularly in AD subjects. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status, nor in SDB‐by‐cognitive status interaction. Moreover, we found no difference in estimated chronological age gap related to SDB, or by‐cognitive status interaction. Contrary to our hypothesis, we were not able to find a general or a diagnostic‐dependent association of SDB with either gray‐matter volumetric or brain aging.

The effects of age on resting‐state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

“…We identified significant whole group effects across all grey matter voxels (Figure 2e). In addition, there was a widespread age‐related decrease in GMV, in bilateral temporal lobes, bilateral prefrontal, middle and superior frontal areas, bilateral medial occipital areas, cerebellum, and subcortical areas including thalamus, caudate, and putamen (Figure 2f), consistent with previous reports (Mohajer et al., 2020; Peelle et al., 2012; Tsvetanov et al., 2020).…”

Daily Morning Blue Light Therapy for Post-mTBI Sleep Disruption: Effects on Brain Structure and Function

“…Total intracranial volume (ICV) was additionally computed using CAT12. Following normalization, gray matter volume estimates were extracted for each of 400 cortical ( 54 ), 36 subcortical ( 55 ), and 37 cerebellar ( 56 ) regions of interest (ROIs) using a concatenation of three well-validated atlases that has been previously used for similar purposes ( 57 ). Each of the ROIs was assigned to one of nine networks based on the Yeo 17-network parcellation [using overarching network names when multiple sub-networks exist; e.g., the default mode A, B, and C networks were labels as DMN; ( 58 )] for the cortical ROIs, one subcortical network, and a cerebellar network.…”

Machine learning for brain age prediction: Introduction to methods and clinical applications

“…Brain age has a range of potential applications for the clinical assessment of individual patients at various stages of health and disease, including the support of diagnosis, prognosis, and treatment decisions ( Figure 3 ). Brain age studies are being conducted in a wide range of clinical populations including neurological conditions such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) [ 1 , 14 , 15 , 25 , 37 , [56] , [57] , [58] , [59] ], traumatic brain injury [ 60 , 61 ], epilepsy [ 18 , 19 , 62 ], multiple sclerosis [ 37 , 54 , 63 ], and stroke [ 64 , 65 ], as well as psychiatric disorders such as schizophrenia [ 10 , 12 , 69 , 20 , 26 , 37 , 38 , 45 , [66] , [67] , [68] ], including clinical high-risk for psychosis (CHR) and first-episode psychosis (FEP), bipolar disorder [ 37 , 38 , 66 , 67 , 70 ], major depressive disorder (MDD) [ 6 , 20 , 32 , 37 , 71 , 72 ], borderline personality disorder [20] , autism spectrum disorder [ 8 , 37 , 73 ], and attention deficit hyperactivity disorder [37] . The results of the clinical studies are summarised in Tables 1 and 2 .…”

“… Authors Clinical group n Age range Mean brain-age gap Mohajer et al. 2020 [58] AD 48 56-91 +9.10 Ly et al. 2020 [14] AD 74 60-85 +6.79 Beheshti et al.…”

“…2010 [1] AD 102 55-88 +10.00 Mohajer et al. 2020 [58] MCI 222 56-91 +4.00 Ly et al. 2020 [14] MCI (early stages) 195 60-85 +1.02 MCI (late stages) 88 60-85 +4.23 Beheshti et al.…”