Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders

Abstract

Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown whether they are linked to dysmaturational processes crossing diagnostic boundaries, aggravating across disease stages, and driving the neurodiagnostic signature of the illness. Therefore, we investigated whether patients with SZ (N = 141), major depression (MD; N = 104), borderline personality disorder (BPD; N = 57), and individuals in at-risk mental states for psychosis (ARMS; N = 89) deviated from the trajectory of normal brain maturation. This deviation was measured as difference between chronological and the neuroanatomical age (brain age gap estimation [BrainAGE]). Neuroanatomical age was determined by a machine learning system trained to individually estimate age from the structural magnetic resonance imagings of 800 healthy controls. Group-level analyses showed that BrainAGE was highest in SZ (+5.5 y) group, followed by MD (+4.0), BPD (+3.1), and the ARMS (+1.7) groups. Earlier disease onset in MD and BPD groups correlated with more pronounced BrainAGE, reaching effect sizes of the SZ group. Second, BrainAGE increased across at-risk, recent onset, and recurrent states of SZ. Finally, BrainAGE predicted both patient status as well as negative and disorganized symptoms. These findings suggest that an individually quantifiable "accelerated aging" effect may particularly impact on the neuroanatomical signature of SZ but may extend also to other mental disorders.

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

“…Table 2 presents the details of the performance of the brain‐age framework using all feature vectors and our proposed feature selection approach as well as comparison to other studies. As can be seen from Table 2, our performance accuracy in terms of the MAE (4.02 years for the HC subjects) is quite comparable to other recent brain‐age estimations of healthy control subjects (Franke & Gaser, 2014; Franke et al., 2010; Gaser et al., 2013; Koutsouleris et al., 2014; Lin et al., 2016; Wang, Dai, Li, Hua, & He, 2012). …”

“…The estimation model in the basis of SVR algorithm has been widely used in different neuroimaging studies (Dosenbach et al., 2011; Erus et al., 2015; Koutsouleris et al., 2014; Lancaster et al., 2018). In the present study, a linear v‐support vector regression (v‐SVR) performed using LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) toolbox with a default setting (i.e., in the LIBSVM: C  = 1, v  =   0.5) was used.…”

“…The authors in (Franke & Gaser, 2012) presented longitudinal alterations in BAS in the 150 AD patients, 112 pMCI patients, 36 sMCI patients, and 108 HCs from the ADNI dataset, where correlation values of r  = −0.46, r  = 0.39 and r  = 0.45 ( p  <   0.001) were achieved between baseline BAS and the MMSE, the CDR and the ADAS scores, respectively. In (Koutsouleris et al., 2014), the researchers examined neuroanatomical age estimation in individuals with schizophrenia and other mental disorders. They trained their proposed brain‐age framework using a structural MRI database of 800 healthy subjects examined at five different centers, and then, they evaluated individuals in at‐risk mental states for psychosis, borderline personality disorder, and major depression and schizophrenia subjects.…”

“…The use of the brain‐age technique has helped reveal the abnormal brain changes in many brain studies such as those of AD (Franke et al., 2010), the prediction of the conversion of mild cognitive impairment (MCI) to AD (Gaser, Franke, Klöppel, Koutsouleris, & Sauer, 2013), and investigations of the brains of children and adolescents (Franke, Luders, May, Wilke, & Gaser, 2012), long‐term meditation practitioners (Luders, Cherbuin, & Gaser, 2016), and schizophrenia patients (Koutsouleris et al., 2014). …”

Leukocyte telomere length: Effects of schizophrenia, age, and gender

“…This has led to a suggestion that schizophrenia is associated with accelerated biological aging (Anthes, 2014;Dawes et al, 2011;Kirkpatrick et al, 2008;Kochunov et al, 2013;Koutsouleris et al, 2014;Lindqvist et al, 2015;Okusaga, 2014;Schnack et al, 2016;Shivakumar et al, 2014). Whereas men have overall higher death rates than women, mortality ratios in schizophrenia (standardized to the general population with respect to age, race/ethnicity and geographic region) are higher in women than in men with schizophrenia, with cardiovascular disease being a leading cause of premature death in both genders (Olfson et al, 2015).…”