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  • Proceedings of the National Academy of Sciences  (13)
  • 1
    Online Resource
    Online Resource
    Dynamic patterns of cortical expansion during folding of the preterm human brain
    Proceedings of the National Academy of Sciences ; 2018
    In:  Proceedings of the National Academy of Sciences Vol. 115, No. 12 ( 2018-03-20), p. 3156-3161
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 12 ( 2018-03-20), p. 3156-3161
    Abstract: During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28–38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1715451115
    RVK:
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    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2018
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 2
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    Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry
    Proceedings of the National Academy of Sciences ; 2020
    In:  Proceedings of the National Academy of Sciences Vol. 117, No. 7 ( 2020-02-18), p. 3808-3818
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 117, No. 7 ( 2020-02-18), p. 3808-3818
    Abstract: The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala–cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1910842117
    RVK:
    TA 1000
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2020
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 3
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    Prenatal exposure to maternal social disadvantage and psychosocial stress and neonatal white matter connectivity at birth
    Proceedings of the National Academy of Sciences ; 2022
    In:  Proceedings of the National Academy of Sciences Vol. 119, No. 42 ( 2022-10-18)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 119, No. 42 ( 2022-10-18)
    Abstract: Early life adversity (social disadvantage and psychosocial stressors) is associated with altered microstructure in fronto-limbic pathways important for socioemotional development. Understanding when these associations begin to emerge may inform the timing and design of preventative interventions. In this longitudinal study, 399 mothers were oversampled for low income and completed social background measures during pregnancy. Measures were analyzed with structural equation analysis resulting in two latent factors: social disadvantage (education, insurance status, income-to-needs ratio [INR], neighborhood deprivation, and nutrition) and psychosocial stress (depression, stress, life events, and racial discrimination). At birth, 289 healthy term-born neonates underwent a diffusion MRI (dMRI) scan. Mean diffusivity (MD) and fractional anisotropy (FA) were measured for the dorsal and inferior cingulum bundle (CB), uncinate, and fornix using probabilistic tractography in FSL. Social disadvantage and psychosocial stress were fitted to dMRI parameters using regression models adjusted for infant postmenstrual age at scan and sex. Social disadvantage, but not psychosocial stress, was independently associated with lower MD in the bilateral inferior CB and left uncinate, right fornix, and lower MD and higher FA in the right dorsal CB. Results persisted after accounting for maternal medical morbidities and prenatal drug exposure. In moderation analysis, psychosocial stress was associated with lower MD in the left inferior CB among the lower-to-higher socioeconomic status (SES) (INR ≥ 200%) group, but not the extremely low SES (INR 〈 200%) group. Increasing access to social welfare programs that reduce the burden of social disadvantage and related psychosocial stressors may be an important target to protect fetal brain development in fronto-limbic pathways.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.2204135119
    RVK:
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2022
    detail.hit.zdb_id: 209104-5
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  • 4
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    Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
    Proceedings of the National Academy of Sciences ; 2023
    In:  Proceedings of the National Academy of Sciences Vol. 120, No. 2 ( 2023-01-10)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 120, No. 2 ( 2023-01-10)
    Abstract: The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.2214634120
    RVK:
    TA 1000
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2023
    detail.hit.zdb_id: 209104-5
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  • 5
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    Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity
    Proceedings of the National Academy of Sciences ; 2013
    In:  Proceedings of the National Academy of Sciences Vol. 110, No. 12 ( 2013-03-19), p. 4768-4773
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 110, No. 12 ( 2013-03-19), p. 4768-4773
    Abstract: 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, high-angular 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.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1216206110
    RVK:
    TA 1000
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2013
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 6
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    Statistical tests and identifiability conditions for pooling and analyzing multisite datasets
    Proceedings of the National Academy of Sciences ; 2018
    In:  Proceedings of the National Academy of Sciences Vol. 115, No. 7 ( 2018-02-13), p. 1481-1486
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 7 ( 2018-02-13), p. 1481-1486
    Abstract: When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer’s disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1719747115
    RVK:
    TA 1000
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    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2018
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 7
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    Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer’s disease pathogenesis
    Proceedings of the National Academy of Sciences ; 2018
    In:  Proceedings of the National Academy of Sciences Vol. 115, No. 8 ( 2018-02-20), p. 1697-1706
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 115, No. 8 ( 2018-02-20), p. 1697-1706
    Abstract: Alzheimer’s disease (AD) is a leading cause of mortality among the elderly. We performed a whole-genome sequencing study of AD in the Chinese population. In addition to the variants identified in or around the APOE locus (sentinel variant rs73052335, P = 1.44 × 10 −14 ), two common variants, GCH1 (rs72713460, P = 4.36 × 10 −5 ) and KCNJ15 (rs928771, P = 3.60 × 10 −6 ), were identified and further verified for their possible risk effects for AD in three small non-Asian AD cohorts. Genotype–phenotype analysis showed that KCNJ15 variant rs928771 affects the onset age of AD, with earlier disease onset in minor allele carriers. In addition, altered expression level of the KCNJ15 transcript can be observed in the blood of AD subjects. Moreover, the risk variants of GCH1 and KCNJ15 are associated with changes in their transcript levels in specific tissues, as well as changes of plasma biomarkers levels in AD subjects. Importantly, network analysis of hippocampus and blood transcriptome datasets suggests that the risk variants in the APOE , GCH1 , and KCNJ15 loci might exert their functions through their regulatory effects on immune-related pathways. Taking these data together, we identified common variants of GCH1 and KCNJ15 in the Chinese population that contribute to AD risk. These variants may exert their functional effects through the immune system.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1715554115
    RVK:
    TA 1000
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    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2018
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
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  • 8
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    Morphometricity as a measure of the neuroanatomical signature of a trait
    Proceedings of the National Academy of Sciences ; 2016
    In:  Proceedings of the National Academy of Sciences Vol. 113, No. 39 ( 2016-09-27)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 113, No. 39 ( 2016-09-27)
    Abstract: Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1604378113
    RVK:
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2016
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 9
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    Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease
    Proceedings of the National Academy of Sciences ; 2016
    In:  Proceedings of the National Academy of Sciences Vol. 113, No. 42 ( 2016-10-18)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 113, No. 42 ( 2016-10-18)
    Abstract: We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1611073113
    RVK:
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2016
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 10
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    Subregional neuroanatomical change as a biomarker for Alzheimer's disease
    Proceedings of the National Academy of Sciences ; 2009
    In:  Proceedings of the National Academy of Sciences Vol. 106, No. 49 ( 2009-12-08), p. 20954-20959
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 106, No. 49 ( 2009-12-08), p. 20954-20959
    Abstract: Regions of the temporal and parietal lobes are particularly damaged in Alzheimer's disease (AD), and this leads to a predictable pattern of brain atrophy. In vivo quantification of subregional atrophy, such as changes in cortical thickness or structure volume, could lead to improved diagnosis and better assessment of the neuroprotective effects of a therapy. Toward this end, we have developed a fast and robust method for accurately quantifying cerebral structural changes in several cortical and subcortical regions using serial MRI scans. In 169 healthy controls, 299 subjects with mild cognitive impairment (MCI), and 129 subjects with AD, we measured rates of subregional cerebral volume change for each cohort and performed power calculations to identify regions that would provide the most sensitive outcome measures in clinical trials of disease-modifying agents. Consistent with regional specificity of AD, temporal-lobe cortical regions showed the greatest disease-related changes and significantly outperformed any of the clinical or cognitive measures examined for both AD and MCI. Global measures of change in brain structure, including whole-brain and ventricular volumes, were also elevated in AD and MCI, but were less salient when compared to changes in normal subjects. Therefore, these biomarkers are less powerful for quantifying disease-modifying effects of compounds that target AD pathology. The findings indicate that regional temporal lobe cortical changes would have great utility as outcome measures in clinical trials and may also have utility in clinical practice for aiding early diagnosis of neurodegenerative disease.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.0906053106
    RVK:
    TA 1000
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2009
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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