The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals

Abstract

Alzheimer's disease (AD) is associated with neurodegeneration in vulnerable limbic and heteromodal regions of the cerebral cortex, detectable in vivo using magnetic resonance imaging. It is not clear whether abnormalities of cortical anatomy in AD can be reliably measured across different subject samples, how closely they track symptoms, and whether they are detectable prior to symptoms. An exploratory map of cortical thinning in mild AD was used to define regions of interest that were applied in a hypothesis-driven fashion to other subject samples. Results demonstrate a reliably quantifiable in vivo signature of abnormal cortical anatomy in AD, which parallels known regional vulnerability to AD neuropathology. Thinning in vulnerable cortical regions relates to symptom severity even in the earliest stages of clinical symptoms. Furthermore, subtle thinning is present in asymptomatic older controls with brain amyloid binding as detected with amyloid imaging. The reliability and clinical validity of AD-related cortical thinning suggests potential utility as an imaging biomarker. This “disease signature” approach to cortical morphometry, in which disease effects are mapped across the cortical mantle and then used to define ROIs for hypothesis-driven analyses, may provide a powerful methodological framework for studies of neuropsychiatric diseases.

The cortical signature of prodromal AD

“…11 Thinning in prodromal AD (the progressor group) was most prominent in the rostral medial temporal cortex, with thinning (compared to nonprogressors) of 0. ROC analysis.…”

“…Longitudinal clinical data after the MRI scan were used to classify subjects into progressors to mild AD dementia (CDR 1) vs nonprogressors. A set of AD signature cortical ROIs-found previously in a separate sample of patients with mild AD (CDR 1) to be consistently affected 11 -was used to extract regional thickness measures which were employed in subsequent analyses. ROI measures were compared in the group of MCI progressors vs nonprogressors, and were also compared to controls and patients with mild AD.…”

“…In addition, we analyzed a measure of the average thickness of these 9 ROIs (average AD-signature ROI thickness), previously shown to be sensitive to early presymptomatic effects of AD. 11 Statistical analyses. The eTIV-adjusted volume and regional thickness measures were normalized to a sample of age-matched controls by calcu-lating a Z score based on the mean and SD of an age-matched group of older controls (OC; CDR ϭ 0) as follows: Z ϭ (x Ϫ age-matched OCs )/ age-matched OCs .…”

Thinning, movement, and volume loss of residual cortical tissue occurs after stroke in the adult rat as identified by histological and magnetic resonance imaging analysis

“…In humans, reduced cortical thickness has also been associated with OCD (Shin et al, 2007), schizophrenia (Kuperberg et al, 2003;Lawyer et al, 2008;Nesvag et al, 2008;Zipursky et al, 1992), amnesia (Seo et al, 2007: Salat et al, 2006), Huntington's disease (Rosas et al, 2002(Rosas et al, , 2005), Alzheimer's disease (Dickerson et al, 2008;Richards et al, 2008), epilepsy (Lee et al, 1995;McDonald et al, 2008), traumatic brain injury (Merkley et al, 2008) and normal ageing (Preul et al, 2006). In many of these cases the severity of disease symptoms and/or cognitive decline was related to the degree of cortical thinning.…”

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

“…Structural magnetic resonance imaging (MRI) can provide high‐resolution measurements of gray and white matter anatomy that are often the focus of within‐ and between‐participant comparisons of aging [see Dickerson et al, 2009; Fjell et al, 2009; Fotenos et al, 2005], development [e.g., Tamnes et al, 2010], clinical disorders [e.g., Cannon et al, 2015; Dickerson et al, 2009; Kempton et al, 2011], and therapeutic intervention [e.g., Bearden et al, 2008; Dazzan et al, 2005]. In practice, structural MRI scans are readily analyzed with convenient, automated image segmentation tools that derive measurements from an individual's regional neuroanatomy (e.g., thickness, surface area, volume), often implemented with freely available software packages [e.g., FreeSurfer [FS], VBM8, FSL‐VBM; Ashburner and Friston, 2000; Dale et al, 1999; Fischl et al, 1999a; Smith et al, 2004] that have been externally validated with manual tracing and post‐mortem analyses [Cardinale et al, 2014; Kennedy et al, 2009; Kuperberg et al, 2003; Rosas et al, 2002; Salat et al, 2004; Sanchez‐Benavides et al, 2010].…”