Bradford C. Dickerson

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer.

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test-retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0.03 mm, and the point-wise standard deviation of measurement error was approximately 0.12 mm. Variability was 0.15 mm and 0.17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1.5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0.2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0.1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD

Objective: To use fMRI to investigate whether hippocampal and entorhinal activation during learning is altered in the earliest phase of mild cognitive impairment (MCI). Methods: Three groups of older individuals were studied: 10 cognitively intact controls, 9 individuals at the mild end of the spectrum of MCI, and 10 patients with probable Alzheimer disease (AD). Subjects performed a face-name associative encoding task during fMRI scanning, and were tested for recognition of stimuli afterward. Data were analyzed using a functional-anatomic method in which medial temporal lobe (MTL) regions of interest were identified from each individuals structural MRI, and fMRI activation was quantified within each region. Results: Significantly greater hippocampal activation was present in the MCI group compared to controls; there were no differences between these two groups in hippocampal or entorhinal volumes. In contrast, the AD group showed hippocampal and entorhinal hypoactivation and atrophy in comparison to controls. The subjects with MCI performed similarly to controls on the fMRI recognition memory task; patients with AD exhibited poorer performance. Across all 29 subjects, greater mean entorhinal activation was found in the subgroup of 13 carriers of the APOE ε4 allele than in the 16 noncarriers. Conclusions: The authors hypothesize that there is a phase of increased medial temporal lobe activation early in the course of prodromal Alzheimer disease followed by a subsequent decrease as the disease progresses.

Alterations in Memory Networks in Mild Cognitive Impairment and Alzheimer's Disease: An Independent Component Analysis

Memory function is likely subserved by multiple distributed neural networks, which are disrupted by the pathophysiological process of Alzheimers disease (AD). In this study, we used multivariate analytic techniques to investigate memory-related functional magnetic resonance imaging (fMRI) activity in 52 individuals across the continuum of normal aging, mild cognitive impairment (MCI), and mild AD. Independent component analyses revealed specific memory-related networks that activated or deactivated during an associative memory paradigm. Across all subjects, hippocampal activation and parietal deactivation demonstrated a strong reciprocal relationship. Furthermore, we found evidence of a nonlinear trajectory of fMRI activation across the continuum of impairment. Less impaired MCI subjects showed paradoxical hyperactivation in the hippocampus compared with controls, whereas more impaired MCI subjects demonstrated significant hypoactivation, similar to the levels observed in the mild AD subjects. We found a remarkably parallel curve in the pattern of memory-related deactivation in medial and lateral parietal regions with greater deactivation in less-impaired MCI and loss of deactivation in more impaired MCI and mild AD subjects. Interestingly, the failure of deactivation in these regions was also associated with increased positive activity in a neocortical attentional network in MCI and AD. Our findings suggest that loss of functional integrity of the hippocampal-based memory systems is directly related to alterations of neural activity in parietal regions seen over the course of MCI and AD. These data may also provide functional evidence of the interaction between neocortical and medial temporal lobe pathology in early AD.

Medial temporal lobe function and structure in mild cognitive impairment

Functional magnetic resonance imaging (fMRI) was used to study memory‐associated activation of medial temporal lobe (MTL) regions in 32 nondemented elderly individuals with mild cognitive impairment (MCI). Subjects performed a visual encoding task during fMRI scanning and were tested for recognition of stimuli afterward. MTL regions of interest were identified from each individuals structural MRI, and activation was quantified within each region. Greater extent of activation within the hippocampal formation and parahippocampal gyrus (PHG) was correlated with better memory performance. There was, however, a paradoxical relationship between extent of activation and clinical status at both baseline and follow‐up evaluations. Subjects with greater clinical impairment, based on the Clinical Dementia Rating Sum of Boxes, recruited a larger extent of the right PHG during encoding, even after accounting for atrophy. Moreover, those who subsequently declined over the 2.5 years of clinical follow‐up (44% of the subjects) activated a significantly greater extent of the right PHG during encoding, despite equivalent memory performance. We hypothesize that increased activation in MTL regions reflects a compensatory response to accumulating AD pathology and may serve as a marker for impending clinical decline. Ann Neurol 2004;56:27–35

MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease ☆

With high resolution, quantitative magnetic resonance imaging (MRI) techniques, it is now possible to examine alterations in brain anatomy in vivo and to identify regions affected in the earliest stages of Alzheimers disease (AD). In this study, we compared MRI-derived entorhinal and hippocampal volume in healthy elderly controls, patients who presented at the clinic with cognitive complaints, but did not meet criteria for dementia (non-demented), and patients with very mild AD. The two patient groups differed significantly from controls in entorhinal volume, but not from each other; in contrast, they differed from each other, as well as from controls, in hippocampal volume, with the mild AD cases showing the greatest atrophy. Follow-up clinical evaluations available on 23/28 non-demented patients indicated that 12/23 had converted to AD within 12-77 months from the baseline MRI examination. Converters could be best differentiated from non-converters on the basis of entorhinal, but not hippocampal volume. These data suggest that although both the EC and hippocampal formation degenerate before the onset of overt dementia, EC volume is a better predictor of conversion.

MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths

Automated MRI-derived measurements of in-vivo human brain volumes provide novel insights into normal and abnormal neuroanatomy, but little is known about measurement reliability. Here we assess the impact of image acquisition variables (scan session, MRI sequence, scanner upgrade, vendor and field strengths), FreeSurfer segmentation pre-processing variables (image averaging, B1 field inhomogeneity correction) and segmentation analysis variables (probabilistic atlas) on resultant image segmentation volumes from older (n=15, mean age 69.5) and younger (both n=5, mean ages 34 and 36.5) healthy subjects. The variability between hippocampal, thalamic, caudate, putamen, lateral ventricular and total intracranial volume measures across sessions on the same scanner on different days is less than 4.3% for the older group and less than 2.3% for the younger group. Within-scanner measurements are remarkably reliable across scan sessions, being minimally affected by averaging of multiple acquisitions, B1 correction, acquisition sequence (MPRAGE vs. multi-echo-FLASH), major scanner upgrades (Sonata-Avanto, Trio-TrioTIM), and segmentation atlas (MPRAGE or multi-echo-FLASH). Volume measurements across platforms (Siemens Sonata vs. GE Signa) and field strengths (1.5 T vs. 3 T) result in a volume difference bias but with a comparable variance as that measured within-scanner, implying that multi-site studies may not necessarily require a much larger sample to detect a specific effect. These results suggest that volumes derived from automated segmentation of T1-weighted structural images are reliable measures within the same scanner platform, even after upgrades; however, combining data across platform and across field-strength introduces a bias that should be considered in the design of multi-site studies, such as clinical drug trials. The results derived from the young groups (scanner upgrade effects and B1 inhomogeneity correction effects) should be considered as preliminary and in need for further validation with a larger dataset.

Functional Alterations in Memory Networks in Early Alzheimer’s Disease

The hallmark clinical symptom of early Alzheimer’s disease (AD) is episodic memory impairment. Recent functional imaging studies suggest that memory function is subserved by a set of distributed networks, which include both the medial temporal lobe (MTL) system and the set of cortical regions collectively referred to as the default network. Specific regions of the default network, in particular, the posteromedial cortices, including the precuneus and posterior cingulate, are selectively vulnerable to early amyloid deposition in AD. These regions are also thought to play a key role in both memory encoding and retrieval, and are strongly functionally connected to the MTL. Multiple functional magnetic resonance imaging (fMRI) studies during memory tasks have revealed alterations in these networks in patients with clinical AD. Similar functional abnormalities have been detected in subjects at-risk for AD, including those with genetic risk and older individuals with mild cognitive impairment. Recently, we and other groups have found evidence of functional alterations in these memory networks even among cognitively intact older individuals with occult amyloid pathology, detected by PET amyloid imaging. Taken together, these findings suggest that the pathophysiological process of AD exerts specific deleterious effects on these distributed memory circuits, even prior to clinical manifestations of significant memory impairment. Interestingly, some of the functional alterations seen in prodromal AD subjects have taken the form of increases in activity relative to baseline, rather than a loss of activity. It remains unclear whether these increases in fMRI activity may be compensatory to maintain memory performance in the setting of early AD pathology or instead, represent evidence of excitotoxicity and impending neuronal failure. Recent studies have also revealed disruption of the intrinsic connectivity of these networks observable even during the resting state in early AD and asymptomatic individuals with high amyloid burden. Research is ongoing to determine if these early network alterations will serve as sensitive predictors of clinical decline, and eventually, as markers of pharmacological response to potential disease-modifying treatments for AD.

Age-related memory impairment associated with loss of parietal deactivation but preserved hippocampal activation

The neural underpinnings of age-related memory impairment remain to be fully elucidated. Using a subsequent memory face–name functional MRI (fMRI) paradigm, young and old adults showed a similar magnitude and extent of hippocampal activation during successful associative encoding. Young adults demonstrated greater deactivation (task-induced decrease in BOLD signal) in medial parietal regions during successful compared with failed encoding, whereas old adults as a group did not demonstrate a differential pattern of deactivation between trial types. The failure of deactivation was particularly evident in old adults who performed poorly on the memory task. These low-performing old adults demonstrated greater hippocampal and prefrontal activation to achieve successful encoding trials, possibly as a compensatory response. Findings suggest that successful encoding requires the coordination of neural activity in hippocampal, prefrontal, and parietal regions, and that age-related memory impairment may be primarily related to a loss of deactivation in medial parietal regions.

Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI

Recent developments in MRI data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. However, a fundamental bottleneck in MRI studies of the hippocampus at the subfield level is that they currently depend on manual segmentation, a laborious process that severely limits the amount of data that can be analyzed. In this article, we present a computational method for segmenting the hippocampal subfields in ultra‐high resolution MRI data in a fully automated fashion. Using Bayesian inference, we use a statistical model of image formation around the hippocampal area to obtain automated segmentations. We validate the proposed technique by comparing its segmentations to corresponding manual delineations in ultra‐high resolution MRI scans of 10 individuals, and show that automated volume measurements of the larger subfields correlate well with manual volume estimates. Unlike manual segmentations, our automated technique is fully reproducible, and fast enough to enable routine analysis of the hippocampal subfields in large imaging studies.