The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attentiondeficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7 -7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone-the so-called ''k-core''-of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1,400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive and psychosocial factors.

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the ‘rich-club’ – a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich-club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich-club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.diffusion tensor imaging | neuroimaging genetics | graph theory | HARDI tractography | multiple comparisons correction H uman brain anatomy involves a complex network of structural and functional pathways that connect anatomically distinct regions. These pathways can be visualized, on a gross anatomical scale, with diffusion imaging-based tractography (1). Neural pathways change as our brains develop (2, 3) and are altered in neurodegenerative conditions, including Alzheimer's disease (4). Our individual genetic makeup exerts a strong influence on the functional synchronization of brain regions (5) and the patterning of cortical structure (6, 7), but particular genes that impact the brain's neural connectivity are still largely unknown.To empower the search for genes that affect the brain's connectivity patterns, we first studied a large twin and family cohort consisting of 366 young adults (ages 20-29 y) from 223 families. We used anatomical brain MRI combined with high-angular resolution diffusion imaging (HARDI) at high magnetic field (4 T) to subdivide cortical regions into areas of known structure and function (8), while also mapping the white matter fiber pathways between them with high-resolution tractography. We defined connectivity maps...

We compare a variety of different anatomical connectivity measures, including several novel ones, that may help in distinguishing Alzheimer’s disease patients from controls. We studied diffusion-weighted MRI from 200 subjects scanned as part of the Alzheimer’s disease Neuroimaging Initiative (ADNI). We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3D lattice. Based on these two kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures’ ability to discriminate disease with a repeated stratified 10-fold cross-validated classifier, using support vector machines (SVMs), a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls, and people with early- or late-stage mild cognitive impairment (MCI), or Alzheimer’s disease (AD).

The planum temporale (PT) is the bank of tissue that lies posterior to Heschl's gyrus and is considered a key brain region involved in language and speech in the human brain. In the human brain, both the surface area and grey matter volume of the PT is larger in the left compared to right hemisphere in approximately 2/3rds of individuals, particularly among right-handed individuals. Here we examined whether chimpanzees show asymmetries in the PT for grey matter volume and surface area in a sample of 103 chimpanzees from magnetic resonance images. The results indicated that, overall, the chimpanzees showed population-level leftward asymmetries for both surface area and grey matter volumes. Furthermore, chimpanzees that prefer to gesture with their right-handed had significantly greater leftward grey matter asymmetries compared to ambiguously-and left-handed apes. When compared to previously published data in humans, the direction and magnitude of PT grey matter asymmetries were similar between humans and apes; however, for the surface area measures, the human showed more pronounced leftward asymmetries. These results suggest that leftward asymmetries in the PT were present in the common ancestor of chimpanzees and humans. Keywords chimpanzees; planum temporale; brain asymmetry; handedness; gestural communication Hemispheric specialization refers to the differential representation of perceptual, motor and cognitive processes by the left and right cerebral hemispheres. Clinical and more recent functional imaging approaches have clearly demonstrated that there many functional asymmetries in the brain, including a number regions associated with the comprehension and production of language [1][2][3][4][5]. The two halves of the brain are also not symmetrical. There are a number of well documented neuroanatomical asymmetries in the human brain [6,7]. Probably no other brain asymmetry has received more attention than the planum temporale (PT). The PT is the flat bank of tissue that lies posterior to Heschl's gyrus in the superior temporal lobe. In one of the first systematic studies of asymmetries in this region, Geschwind and LeMay [8] quantified the PT on the left and right hemispheres in a sample of 100 post-mortem brains © 2009 Elsevier B.V. All rights reserved.Correspondence concerning this article should be addressed to Dr. William Hopkins, Department of Psychology, Agnes Scott College, 141 E. College Avenue, Decatur, GA 30030. whopkins@agnesscott.edu or whopkin@emory.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. and found that the left side was larger than...