Faculty Bibliography

Recently, a great deal of interest has arisen in resting state fMRI as a measure of tonic brain function in clinical populations. Most studies have focused on the examination of temporal correlation between resting state fMRI low-frequency oscillations (LFOs). Studies on the amplitudes of these low-frequency oscillations are rarely reported. Here, we used amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF; the relative amplitude that resides in the low frequencies) to examine the amplitude of LFO in schizophrenia. Twenty-six healthy controls and 29 patients with schizophrenia or schizoaffective disorder participated. Our findings show that patients showed reduced low-frequency amplitude in proportion to the total frequency band investigated (i.e., fALFF) in the lingual gyrus, left cuneus, left insula/superior temporal gyrus, and right caudate and increased fALFF in the medial prefrontal cortex and the right parahippocampal gyrus. ALFF was reduced in patients in the lingual gyrus, cuneus, and precuneus and increased in the left parahippocampal gyrus. These results suggest LFO abnormalities in schizophrenia. The implication of these abnormalities for schizophrenic symptomatology is further discussed

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's 'functional connectome.' Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/

Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-related a priori time courses, with 'back-reconstruction' from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1) single-voxel, (2) few-voxel, and (3) many-voxel seed; and dual-regression-based with (4) single ICA map and (5) multiple ICA map seed

OBJECTIVE: The polymorphism BDNF val66met of the brain derived neurotrophic factor (BDNF) is common, may increase the risk for depression, and affects BDNF secretion, critical for neuronal survival, plasticity, neurogenesis, and synaptic connectivity. Our objectives were: 1) to test the hypothesis that BDNF(val/met) status influences the remission rate of geriatric depression; 2) to explore whether the relationship between BDNF allelic status to remission is influenced by the presence of microstructural white matter abnormalities. METHOD: Non-demented older subjects with major depression had a 2-week placebo period, after which those with a Hamilton Depression Rating Scale (HDRS) of 18 or greater received escitalopram 10 mg daily for 12 weeks. Fractional anisotropy was determined in specific regions using the Reproducible Object Quantification Scheme (ROQS) software that operates on non-normalized data. RESULTS: BDNF(met) carriers were more likely to achieve remission than BDNF(val/val) homozygotes after 12 weeks of treatment with escitalopram 10 mg daily. Microstructural abnormalities in the corpus callosum, left superior corona radiata, and right inferior longitudinal fasciculum were also associated with lower remission rate. However, there were no significant interactions between BDNF(val66met) status and microstructural abnormalities in predicting remission. LIMITATIONS: Small number of subjects, focus on a single BDNF polymorphism, fixed antidepressant dose. CONCLUSIONS: Depressed older BDNF(met) carriers had a higher remission rate than BDNF(val/val) homozygotes. This effect was not related to microstructural white matter abnormalities, which predicted remission independently. We speculate that the relationship between BDNF(val66met) and remission is due to different effects of BDNF in brain structures related to mood regulation

Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to motion and physiological sources, negatively impact the functional signal-to-noise ratio in fMRI studies, even after conventional fMRI preprocessing. Independent component analysis' demonstrated capacity to separate sources of neural signal, structured noise, and random noise into separate components might be utilized in improved procedures to remove artifacts from fMRI data. Such procedures require a method for labeling independent components (ICs) as representing artifacts to be removed or neural signals of interest to be spared. Visual inspection is often considered an accurate method for such labeling as well as a standard to which automated labeling methods are compared. However, detailed descriptions of methods for visual inspection of ICs are lacking in the literature. Here we describe the details of, and the rationale for, an operationalized fMRI data denoising procedure that involves visual inspection of ICs (96% inter-rater agreement). We estimate that dozens of subjects/sessions can be processed within a few hours using the described method of visual inspection. Our hope is that continued scientific discussion of and testing of visual inspection methods will lead to the development of improved, cost-effective fMRI denoising procedures

BACKGROUND: Cerebrovascular disease may increase vulnerability to geriatric depression, a syndrome often accompanied by frontal-subcortical lesions. High blood pressure is a risk factor for cerebrovascular disease and white matter changes. This study examined whether and in which brain regions blood pressure is associated with compromised white matter integrity in elderly depressed patients. METHODS: We studied the association between blood pressure and white matter integrity assessed by diffusion tensor imaging (fractional anisotropy, FA) in 41 older patients with major depression. Correlations between FA and blood pressure, after controlling for age, were examined with a voxelwise analysis. RESULTS: Significant associations between FA and blood pressure were detected throughout the anterior cingulate and in multiple frontostriatal and frontotemporal regions. LIMITATIONS: This study did not employ a healthy control group. Moreover, the relatively small sample size precluded a comparison of patients with and without hypertension. CONCLUSIONS: Compromised frontal-striatal white matter integrity may be the anatomical background through which blood pressure confers vulnerability to depression

BACKGROUND: Structural abnormalities of the anterior cingulate cortex (ACC) may interfere with the interaction of cortical and limbic networks involved in emotional regulation and contribute to chronic depressive syndromes in the elderly. This study examined the relationship of regional anterior cingulate cortical volumes with treatment remission of elderly depressed patients. We hypothesized that patients who failed to remit during a 12-week controlled treatment trial of escitalopram would exhibit smaller anterior cingulate gray matter volumes than patients who remitted. METHODS: The participants were 41 non-demented individuals with non-psychotic major depression. After a 2-week single-blind placebo period, subjects who still had a Hamilton Depression Rating Scale (HDRS) of 18 or greater received escitalopram 10 mg daily for 12 weeks. Remission was defined as a HDRS score of 7 or below for at least 2 consecutive weeks. The patient sample consisted of 22 depressed patients who achieved remission during the study and 19 depressed patients who remained symptomatic. High-resolution magnetization-prepared rapidly acquired gradient echo (MPRAGE) sequences were acquired on a 1.5 T scanner and regional ACC volumes were manually outlined (dorsal, rostral, anterior subgenual, and posterior subgenual). RESULTS: Repeated measure analyses revealed that patients who failed to remit following escitalopram treatment had smaller dorsal and rostral anterior cingulate gray matter volumes than patients who remitted, whereas subgenual cortical volumes did not differ between the groups. CONCLUSIONS: Structural abnormalities of the dorsal and rostral anterior cingulate may perpetuate late-life depression

OBJECTIVE: This study compared microstructural abnormalities in depressed elders and controls and studied the association of the serotonin transporter gene status to white matter abnormalities and to remission of depression. METHODS: The subjects were Caucasians with non-psychotic major depression and normal elders. Depressed subjects received escitalopram 10 mg daily for 12 weeks. Remission was defined as a HDRS score of 7 or below for 2 consecutive weeks. Diffusion tensor imaging was performed and voxel-based analysis of fractional anisotropy (FA) was conducted using age and mean diffusivity as covariates. RESULTS: Depressed elders (N=27) had lower FA than controls (N=27) in several frontolimbic areas. Depressed elderly S-allele carriers also had lower FA than L homozygotes in frontolimbic brain areas, including the dorsal and rostral anterior cingulate, posterior cingulate, dorsolateral prefrontal and medial prefrontal regions, thalamus, and in other regions. S-allele carriers had a lower remission rate than L homozygotes. LIMITATIONS: Small number of subjects, lack of random sampling, fixed antidepressant dose, short follow-up. CONCLUSIONS: Lower FA was observed in several frontolimbic and other regions in depressed elders compared to controls. Depressed S-allele carriers had both microstructural white matter abnormalities in frontolimbic networks and a low remission rate. It remains unclear whether the risk for chronicity of geriatric depression in S-allele carriers is mediated by frontolimbic compromise. However, these observations set the stage for studies aiming to identify the relationship of S allele to impairment in specific frontolimbic functions interfering with response of geriatric depression to antidepressants