Richard S. J. Frackowiak

Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease

Large, multi-site studies utilizing MRI-derived measures from multiple scanners present an opportunity to advance research by pooling data. On the other hand, it remains unclear whether or not the potential confound introduced by different scanners and upgrades will devalue the integrity of any results. Although there are studies of scanner differences for the purpose of calibration and quality control, the current literature is devoid of studies that describe the analysis of multi-scanner data with regard to the interaction of scanner(s) with effects of interest. We investigated a data-set of 136 subjects, 62 patients with mild to moderate Alzheimers disease and 74 cognitively normal elderly controls, with MRI scans from one center that were acquired over 10 years with 6 different scanners and multiple upgrades over time. We used a whole-brain voxel-wise analysis to evaluate the effect of scanner, effect of disease, and the interaction of scanner and disease for the 6 different scanners. The effect of disease in patients showed the expected significant reduction of grey matter in the medial temporal lobe. Scanner differences were substantially less than the group differences and only significant in the thalamus. There was no significant interaction of scanner with disease group. We describe the rationale for concluding that our results were not confounded by scanner differences. Similar analyses in other multi-scanner data-sets could be used to justify the pooling of data when needed, such as in studies of rare disorders or in multi-center designs.

Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps

Basal ganglia and brain stem nuclei are involved in the pathophysiology of various neurological and neuropsychiatric disorders. Currently available structural T1-weighted (T1w) magnetic resonance images do not provide sufficient contrast for reliable automated segmentation of various subcortical grey matter structures. We use a novel, semi-quantitative magnetization transfer (MT) imaging protocol that overcomes limitations in T1w images, which are mainly due to their sensitivity to the high iron content in subcortical grey matter. We demonstrate improved automated segmentation of putamen, pallidum, pulvinar and substantia nigra using MT images. A comparison with segmentation of high-quality T1w images was performed in 49 healthy subjects. Our results show that MT maps are highly suitable for automated segmentation, and so for multi-subject morphometric studies with a focus on subcortical structures.

Neural basis of generation of conclusions in elementary deduction

In everyday life, people untrained in formal logic draw simple deductive inferences from linguistic material (i.e., elementary propositional deductions). Presently, we have limited information on the brain areas implicated when such conclusions are drawn. We used event-related fMRI to identify these brain areas. A set of multiple and independent criteria was derived from the two main theories in the field of reasoning to maximize the reliability of detection of areas in which activity is specifically associated with deductive inferences. Two left lateralized clusters of areas, one in frontal cortex (Brodmann Area 44 and 6) and one in parietal lobe (BA 40), satisfied all criteria; activation was present at the moment of inference, it was shared by both conditional (if-based) and disjunctive (or-based) inferences but was greater for disjunctive clauses. Identification of the reasoning network was corroborated by the observation that activity in these areas was greater the longer the reasoning time. Taken together with results from preceding studies, our findings suggest possible theoretically relevant dissociations between elementary propositional deductions and other types of deductive reasoning.

Genotype–phenotype interactions in primary dystonias revealed by differential changes in brain structure

Our understanding of how genotype determines phenotype in primary dystonia is limited. Familial young-onset primary dystonia is commonly due to the DYT1 gene mutation. A critical question, given the 30% penetrance of clinical symptoms in DYT1 mutation carriers, is why the same genotype leads to differential clinical expression and whether non-DYT1 adult-onset primary dystonia, with and without family history share pathophysiological mechanisms with DYT1 dystonia. This study examines the relationship between dystonic phenotype and the DYT1 gene mutation by monitoring whole-brain structure using voxel-based morphometry. We acquired magnetic resonance imaging data of symptomatic and asymptomatic DYT1 mutation carriers, of non-DYT1 primary dystonia patients, with and without family history and control subjects with normal DYT1 alleles. By crossing the factors genotype and phenotype we demonstrate a significant interaction in terms of brain anatomy confined to the basal ganglia bilaterally. The explanation for this effect differs according to both gene and dystonia status: non-DYT1 adult-onset dystonia patients and asymptomatic DYT1 carriers have significantly larger basal ganglia compared to healthy subjects and symptomatic DYT1 mutation carriers. There is a significant negative correlation between severity of dystonia and basal ganglia size in DYT1 mutation carriers. We propose that differential pathophysiological and compensatory mechanisms lead to brain structure changes in non-DYT1 primary adult-onset dystonias and DYT1 gene carriers. Given the range of age of onset, there may be differential genetic modulation of brain development that in turn determines clinical expression. Alternatively, a DYT1 gene dependent primary defect of motor circuit development may lead to stress-induced remodelling of the basal ganglia and hence dystonia.

A plea for confidence intervals and consideration of generalizability in diagnostic studies

Sir, Many thanks for letting us respond to the interesting letter concerning our recent paper. We are grateful for the chance to clarify the points raised, which suggest our conclusions were too optimistic. In our paper (Kloppel et al. , 2008), we used MRI scans from pathologically proven cases of Alzheimers disease and frontotemporal lobar degeneration (FTLD) to validate trained sets for a machine learning-based support vector machine (SVM) approach to the categorization of structural scans from normal and each other.nnThis rigorous approach substantially limited the number of available subjects, which we made perfectly clear in our article, but which was unavoidable given our novel approach. Frost and colleagues are right to point out that such low numbers result in larger confidence intervals than if we were able to include more scans. This is an object of our further empirical studies—what is the improvement in classification gained using this technique with greater numbers of scans in the trained set? The graph below (Fig. 1 …

Imaging Functional Reorganization after Brain Injury

Stroke is one of the most common afflictions of patients with neurological symptoms. About 2, 400 per 1 million people suffer from a stroke annually. Stroke is a severe disabling disorder and a major socioeconomic health problem. Rehabilitation of stroke patients is a difficult task and requires large interdisciplinary cooperation.