Clare E. Mackay

Correspondence of the brain's functional architecture during activation and rest.

Neural connections, providing the substrate for functional networks, exist whether or not they are functionally active at any given moment. However, it is not known to what extent brain regions are continuously interacting when the brain is “at rest.” In this work, we identify the major explicit activation networks by carrying out an image-based activation network analysis of thousands of separate activation maps derived from the BrainMap database of functional imaging studies, involving nearly 30,000 human subjects. Independently, we extract the major covarying networks in the resting brain, as imaged with functional magnetic resonance imaging in 36 subjects at rest. The sets of major brain networks, and their decompositions into subnetworks, show close correspondence between the independent analyses of resting and activation brain dynamics. We conclude that the full repertoire of functional networks utilized by the brain in action is continuously and dynamically “active” even when at “rest.”

Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele.

The APOE ε4 allele is a risk factor for late-life pathological changes that is also associated with anatomical and functional brain changes in middle-aged and elderly healthy subjects. We investigated structural and functional effects of the APOE polymorphism in 18 young healthy APOE ε4-carriers and 18 matched noncarriers (age range: 20–35 years). Brain activity was studied both at rest and during an encoding memory paradigm using blood oxygen level-dependent fMRI. Resting fMRI revealed increased “default mode network” (involving retrosplenial, medial temporal, and medial-prefrontal cortical areas) coactivation in ε4-carriers relative to noncarriers. The encoding task produced greater hippocampal activation in ε4-carriers relative to noncarriers. Neither result could be explained by differences in memory performance, brain morphology, or resting cerebral blood flow. The APOE ε4 allele modulates brain function decades before any clinical or neurophysiological expression of neurodegenerative processes.

Antidepressant drug treatment modifies the neural processing of nonconscious threat cues.

BACKGROUND The amygdala is believed to play a key role in processing emotionally salient, threat-relevant, events that require further online processing by cortical regions. Emotional disorders such as depression and anxiety have been associated with hyperactivity of the amygdala, but it is unknown whether antidepressant treatment directly affects amygdala responses to emotionally significant information. METHODS The current study assessed the effects of 7 days administration of the selective serotonin reuptake inhibitor (SSRI), citalopram, on amygdala responses to masked presentations of fearful and happy facial expressions in never-depressed volunteers using blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging. A double-blind, between-groups design was used with volunteers randomized to 20 mg/day citalopram versus placebo. RESULTS Volunteers receiving citalopram showed decreased amygdala responses to masked presentations of threat compared with those receiving placebo. Citalopram also reduced responses within the hippocampus and medial prefrontal cortex (mPFC) specifically during the fear-relevant stimuli. These neural differences were accompanied by decreased recognition of fearful facial expressions assessed after the scan. By contrast, there was no effect of citalopram on the neural or behavioral response to the happy facial expressions. CONCLUSIONS These results suggest a direct effect of serotonin potentiation on amygdala response to threat-relevant stimuli in humans. Such effects may be important in the therapeutic actions of antidepressants in depression and anxiety.

Group comparison of resting-state FMRI data using multi-subject ICA and dual regression

➁ Sensitivity to global amplitude differences: Signal ‘L’ is twice as strong in data set B compared to data set A. Back-projected spatial maps for ‘L’ are identical (i.e. no detection of a significant difference between data sets A and B; false negative detection). By comparison, the dual regression results (with time series normalisation) reflect differences in the global amplitude between the data sets (true positive detection). Dual Regression in context

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIBs ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

Between session reproducibility and between subject variability of diffusion MR and tractography measures.

As diffusion tractography is increasingly used to generate quantitative measures to address clinical questions, it is important to characterise the inter-session reproducibility and inter-subject variability of these measures. Here, we assess the reproducibility and variability of diffusion tractography measures using diffusion data from 8 subjects scanned 3 times. We used probabilistic tractography to define the cingulum bundle, pyramidal tracts, optic radiations and genu of the corpus callosum in each individual data set using three different methods of seed definition. Measures of mean fractional anisotropy (FA) and mean diffusivity (MD) along the tracts were more reproducible than measures of tract volume. Further, tracts defined using a two region of interest (ROI) approach were more reproducible than those defined using manually placed seed masks alone. For mean FA taken from tracts defined using the two ROI approach, inter-session coefficients of variation (CV) were all below 5% and inter-subject CVs were below 10%; for mean MD inter-session, CVs were all below 3% and inter-subject CVs were below 8%. We use the variability measures found here to calculate the sample sizes required to detect changes in FA, MD or tract volume of a given size, either between groups of subjects or within subjects over time. Finally, we compare tractography results using 60 diffusion encoding directions to those found using a subset of 12 directions; the number of diffusion directions did not have a significant effect on reproducibility, but tracts derived using fewer directions were consistently smaller than those derived using 60 direction data. We suggest that 12 direction data are sufficient for reproducibly defining the core of large bundles but may be less sensitive to smaller pathways.

A Systematic Review of Diffusion Tensor Imaging Studies in Affective Disorders

White matter abnormalities constitute one element of the network dysfunction that underlies affective disorders: differences between the white matter of subjects with affective disorders and control subjects have been identified using a range of neuroimaging and histological techniques. Diffusion tensor imaging (DTI) can uniquely study the orientation and integrity of white matter tracts and is thus an ideal tool to shed light on white matter abnormalities in subjects with affective disorders. Here, we systematically review DTI studies of affective disorders. We identified DTI studies of affective disorders from EMBASE and MEDLINE and searched the reference lists of relevant papers. Twenty-seven articles comparing subjects with affective disorders with control subjects were included in the review, with eight studies included in a meta-analysis of superior frontal regions. Twenty-one of 27 studies found significantly lower anisotropy in subjects with affective disorders compared with control subjects, more specifically within the frontal and temporal lobes or tracts. A large effect size was detected within the superior frontal gyrus, although heterogeneity and one index of publication bias were significant. Although there is significant heterogeneity of acquisition and analysis methods and subject properties, DTI studies of affective disorders consistently identify reduced anisotropy in the frontal and temporal lobes and tracts of subjects with affective disorders relative to control subjects.

Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis

Objective: While the hallmark of amyotrophic lateral sclerosis (ALS) is corticospinal tract in combination with lower motor neuron degeneration, the clinical involvement of both compartments is characteristically variable and the site of onset debated. We sought to establish whether there is a consistent signature of cerebral white matter abnormalities in heterogeneous ALS cases. Methods: In this observational study, diffusion tensor imaging was applied in a whole-brain analysis of 24 heterogeneous patients with ALS and well-matched healthy controls. Tract-based spatial statistics were used, with optimized voxel-based morphometry of T1 images to determine any associated gray matter involvement. Results: A consistent reduction in fractional anisotropy was demonstrated in the corpus callosum of the ALS group, extending rostrally and bilaterally to the region of the primary motor cortices, independent of the degree of clinical upper motor neuron involvement. Matched regional radial diffusivity increase supported the concept of anterograde degeneration of callosal fibers observed pathologically. Gray matter reductions were observed bilaterally in primary motor and supplementary motor regions, and also in the anterior cingulate and temporal lobe regions. A post hoc group comparison model incorporating significant values for fractional anisotropy, radial diffusivity, and gray matter was 92% sensitive, 88% specific, with an accuracy of 90%. Conclusion: Callosal involvement is a consistent feature of ALS, independent of clinical upper motor neuron involvement, and may reflect independent bilateral cortical involvement or interhemispheric spread of pathology. The predominantly rostral corticospinal tract involvement further supports the concept of independent cortical degeneration even in those patients with ALS with predominantly lower motor neuron involvement clinically.

Voxel-based morphometric comparison of hippocampal and extrahippocampal abnormalities in patients with left and right hippocampal atrophy.

We used voxel-based morphometry (VBM), an automatic whole-brain MR image analysis technique, to investigate gray matter abnormalities in patients with temporal lobe epilepsy (TLE), in whom hippocampal atrophy (HA) was demonstrated by application of the Cavalieri method of modern design stereology. VBM results (P < 0.05, corrected) indicated preferential gray matter concentration (GMC) reduction in anterior hippocampus in patients with left HA and posterior hippocampus in patients with right HA. GMC reduction was also found in right dorsal prefrontal cortex in left and right HA patients. Prefrontal atrophy may be due to epileptiform excitotoxic discharges from the reciprocally connected pathological hippocampus, and may be the underlying biological cause for executive dysfunction in patients with TLE. GMC excess in ipsilateral parahippocampal, cerebellar, and pericallosal regions was common to both left and right HA groups relative to controls, and is hypothesized to reflect diminished gray-white matter demarcation, underlying white matter atrophy, or structural displacement due to cerebrospinal fluid expansion. However, bilateral temporal lobe GMC excess was observed in left HA patients, while ipsilateral temporal lobe GMC excess was observed in right HA patients. This work demonstrates methodological consistency between automated VBM and manual stereological analysis of the hippocampus in group comparisons, indicates widespread extrahippocampal gray matter abnormalities in unilateral HA, and suggests that there may be inherent differences in the effect of TLE on temporal lobe structures depending on the side of HA.

Connectivity-based parcellation of human cortex using diffusion MRI: Establishing reproducibility, validity and observer independence in BA 44/45 and SMA/pre-SMA.

The identification of specialized, functional regions of the human cortex is a vital precondition for neuroscience and clinical neurosurgery. Functional imaging modalities are used for their delineation in living subjects, but these methods rely on subject cooperation, and many regions of the human brain cannot be activated specifically. Diffusion tractography is a novel tool to identify such areas in the human brain, utilizing underlying white matter pathways to separate regions of differing specialization. We explore the reproducibility, generalizability and validity of diffusion tractography-based localization in four functional areas across subjects, timepoints and scanners, and validate findings against fMRI and post-mortem cytoarchitectonic data. With reproducibility across modalities, clustering methods, scanners, timepoints, and subjects in the order of 80-90%, we conclude that diffusion tractography represents a useful and objective tool for parcellation of the human cortex into functional regions, enabling studies into individual functional anatomy even when there are no specific activation paradigms available.