Altered metabolic connectivity within the limbic cortico-striato-thalamo-cortical circuit in presymptomatic and symptomatic behavioral variant frontotemporal dementia.

BACKGROUND: Behavioral variant frontotemporal dementia (bvFTD) is predominantly considered a dysfunction in cortico-cortical transmission, with limited direct investigation of cortical-subcortical transmission. Thus, we aimed to characterize the metabolic connectivity between areas of the limbic cortico-striato-thalamic-cortical (CSTC) circuit in presymptomatic and symptomatic bvFTD patients.

METHODS: Thirty-three bvFTD patients and 33 unrelated healthy controls were recruited for this study. Additionally, six asymptomatic carriers of the MAPT P301L mutation were compared with 12 non-carriers who were all from the same family of bvFTD. Each participant underwent neuropsychological assessment, genetic testing, and a hybrid PET/MRI scan. Seed-based metabolic connectivity based on [18 F]-fluorodeoxyglucose PET between the main components within the limbic CSTC circuit was explored according to the Oxford-GSK-Imanova Striatal Connectivity Atlas.

RESULTS: BvFTD patients exhibited reduced metabolic connectivity between the relays in the limbic CSTC circuit, which included the frontal region (ventromedial prefrontal cortex, orbitofrontal cortex, rectus gyrus, and anterior cingulate cortex), the limbic striatum, and thalamus compared to controls. In the bvFTD patients, the involvement of the limbic CSTC circuit was associated with the severity of behavior disruption, as measured by the frontal behavior inventory, the disinhibition subscale, and the apathy subscale. Notably, asymptomatic MAPT carriers had weakened frontostriatal connectivity but enhanced striatothalamus and thalamofrontal connectivity within the limbic CSTC circuit compared with noncarriers.

CONCLUSION: These findings suggested that aberrant metabolic connectivity within the limbic CSTC circuit is present in symptomatic and even asymptomatic stages of bvFTD. Thus, metabolic connectivity patterns could be used as a potential biomarker to detect the presymptomatic stage and track disease progression.