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Thirty-nine elderly depressed patients as well as 15 demented patients with Alzheimer's disease and 11 healthy volunteers were imaged at rest with a high resolution single-slice 12-detector head scanner (SME-Neuro 900) and the cerebral perfusion marker 99mTc-Exametazime (HM-PAO). Statistical parametric maps were computed to compare early- and late-onset depressed, Alzheimer patients and healthy volunteers and to examine associations between regional perfusion and clinical and MRI variables. Patients with late-onset depression showed reductions in temporal lobe perfusion compared with early-onset depression and controls. Alzheimer patients had the expected reduced perfusion in temporoparietal and prefontal cortex, as well as basal ganglia, compared with healthy controls. Compared with depressed patients, they showed a relative reduction in temporoparietal cortex, only. This difference was more pronounced between Alzheimer patients and early onset, compared to late-onset patients with depression. Periventricular white matter changes on MRI were associated with temporal lobe reductions of tracer uptake in depression. In the Alzheimer group, deep white matter MRI changes were associated with frontal perfusion deficits. Our results support a vulnerability hypothesis, which predicts that patients with late-onset depression will show more brain changes than patients with an early onset of their illness. Statistical parametric mapping in patients with organic psychiatric brain syndromes is feasible and promising as a clinical and research method.

Original publication

DOI

10.1006/nimg.1998.0321

Type

Journal article

Journal

Neuroimage

Publication Date

04/1998

Volume

7

Pages

199 - 208

Keywords

Aged, Aged, 80 and over, Alzheimer Disease, Basal Ganglia, Brain, Brain Mapping, Cerebral Cortex, Depressive Disorder, Major, Diagnosis, Differential, Dominance, Cerebral, Feasibility Studies, Humans, Image Processing, Computer-Assisted, Mathematical Computing, Middle Aged, Reference Values, Technetium Tc 99m Exametazime, Tomography, Emission-Computed, Single-Photon