TY  - JOUR
AB  - OBJECTIVE: Insulin resistance during childhood is a risk factor for developing type 2 diabetes and other health problems later in life. Studies in adults have shown that insulin resistance affects regional and network activity in the brain which are vital for behavior, e.g. ingestion and metabolic control. To date, no study has investigated whether brain responses to food cues in children are associated with peripheral insulin sensitivity. METHODS: We included 53 children (36 girls) between the age of 7-11 years, who underwent an oral Glucose Tolerance Test (oGTT) to estimate peripheral insulin sensitivity (ISI). Brain responses were measured using functional magnetic resonance imaging (fMRI) before and after glucose ingestion. We compared food-cue task-based activity and functional connectivity (FC) between children with low and high ISI, adjusted for age and BMIz. RESULTS: Independent of prandial state (i.e., glucose ingestion), children with lower ISI showed higher FC between the anterior insula and caudate and lower FC between the posterior insula and mid temporal cortex than children with higher ISI. Sex differences were found based on prandial state and peripheral insulin sensitivity in the insular FC. No differences were found on whole-brain food-cue reactivity. CONCLUSIONS: Children with low peripheral insulin sensitivity showed differences in food cue evoked response particularly in insula functional connectivity. These differences might influence eating behavior and future risk of developing diabetes.
AU  - Semeia, L.
AU  - Veit, R.
AU  - Zhao, S.
AU  - Luo, S.*
AU  - Angelo, B.*
AU  - Birkenfeld, A.L.
AU  - Preissl, H.
AU  - Xiang, A.H.*
AU  - Kullmann, S.
AU  - Page, K.A.*
C1  - 70118
C2  - 56870
CY  - 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa
TI  - Influence of insulin sensitivity on food cue evoked functional brain connectivity in children.
JO  - Neuroimage
VL  - 310
PB  - Academic Press Inc Elsevier Science
PY  - 2025
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - When does the mind begin? Infant psychology is mysterious in part because we cannot remember our first months of life, nor can we directly communicate with infants. Even more speculative is the possibility of mental life prior to birth. The question of when consciousness, or subjective experience, begins in human development thus remains incompletely answered, though boundaries can be set using current knowledge from developmental neurobiology and recent investigations of the perinatal brain. Here, we offer our perspective on how the development of a sensory perturbational complexity index (sPCI) based on auditory ("beep-and-zip"), visual ("flash-and-zip"), or even olfactory ("sniff-and-zip") cortical perturbations in place of electromagnetic perturbations ("zap-and-zip") might be used to address this question. First, we discuss recent studies of perinatal cognition and consciousness using techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and, in particular, magnetoencephalography (MEG). While newborn infants are the archetypal subjects for studying early human development, researchers may also benefit from fetal studies, as the womb is, in many respects, a more controlled environment than the cradle. The earliest possible timepoint when subjective experience might begin is likely the establishment of thalamocortical connectivity at 26 weeks gestation, as the thalamocortical system is necessary for consciousness according to most theoretical frameworks. To infer at what age and in which behavioral states consciousness might emerge following the initiation of thalamocortical pathways, we advocate for the development of the sPCI and similar techniques, based on EEG, MEG, and fMRI, to estimate the perinatal brain's state of consciousness.
AU  - Fröhlich, J.*
AU  - Bayne, T.*
AU  - Crone, J.S.*
AU  - DallaVecchia, A.*
AU  - Kirkeby-Hinrup, A.*
AU  - Mediano, P.A.M.*
AU  - Moser, J.
AU  - Talar, K.*
AU  - Gharabaghi, A.*
AU  - Preissl, H.
C1  - 67640
C2  - 53947
CY  - 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa
TI  - Not with a "zap" but with a "beep": Measuring the origins of perinatal experience.
JO  - Neuroimage
VL  - 273
PB  - Academic Press Inc Elsevier Science
PY  - 2023
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
AU  - Haugg, A.*
AU  - Renz, F.M.*
AU  - Nicholson, A.A.*
AU  - Lor, C.*
AU  - Götzendorfer, S.J.*
AU  - Sladky, R.*
AU  - Skouras, S.*
AU  - McDonald, A.*
AU  - Craddock, C.*
AU  - Hellrung, L.*
AU  - Kirschner, M.*
AU  - Herdener, M.*
AU  - Koush, Y.*
AU  - Papoutsi, M.*
AU  - Keynan, J.N.*
AU  - Hendler, T.*
AU  - Cohen Kadosh, K.*
AU  - Zich, C.*
AU  - Kohl, S.H.*
AU  - Hallschmid, M.
AU  - MacInnes, J.*
AU  - Adcock, R.A.*
AU  - Dickerson, K.C.*
AU  - Chen, N.K.*
AU  - Young, K.*
AU  - Bodurka, J.*
AU  - Marxen, M.*
AU  - Yao, S.*
AU  - Becker, B.*
AU  - Auer, T.*
AU  - Schweizer, R.*
AU  - Pamplona, G.*
AU  - Lanius, R.A.*
AU  - Emmert, K.*
AU  - Haller, S.*
AU  - van de Ville, D.*
AU  - Kim, D.Y.*
AU  - Lee, J.H.*
AU  - Marins, T.*
AU  - Megumi, F.*
AU  - Sorger, B.*
AU  - Kamp, T.*
AU  - Liew, S.L.*
AU  - Veit, R.
AU  - Spetter, M.*
AU  - Weiskopf, N.*
AU  - Scharnowski, F.*
AU  - Steyrl, D.*
C1  - 62179
C2  - 50681
CY  - 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa
TI  - Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis.
JO  - Neuroimage
VL  - 237
PB  - Academic Press Inc Elsevier Science
PY  - 2021
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected only in the structured condition, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in areas around the left pre-central gyrus as well as right temporo-frontal areas significantly predicted behavioral performance. Sensor level results confirmed this relationship between neural entrainment and subsequent explicit knowledge. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two aspects are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on factors such as sufficient exposure time and attention.
AU  - Moser, J.
AU  - Batterink, L.*
AU  - Hegner, Y.L.*
AU  - Schleger, F.
AU  - Braun, C.*
AU  - Paller, K.A.*
AU  - Preissl, H.
C1  - 62512
C2  - 50897
CY  - 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa
TI  - Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge.
JO  - Neuroimage
VL  - 240
PB  - Academic Press Inc Elsevier Science
PY  - 2021
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Human brain atlases provide spatial reference systems for data characterizing brain organization at different levels, coming from different brains. Cytoarchitecture is a basic principle of the microstructural organization of the brain, as regional differences in the arrangement and composition of neuronal cells are indicators of changes in connectivity and function. Automated scanning procedures and observer-independent methods are prerequisites to reliably identify cytoarchitectonic areas, and to achieve reproducible models of brain segregation. Time becomes a key factor when moving from the analysis of single regions of interest towards high-throughput scanning of large series of whole-brain sections. Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains. It is based on a Deep Convolutional Neural Network (CNN), which is trained on a pair of section images with annotations, with a large number of un-annotated sections in between. The model learns to create all missing annotations in between with high accuracy, and faster than our previous workflow based on observer-independent mapping. The new workflow does not require preceding 3D-reconstruction of sections, and is robust against histological artefacts. It processes large data sets with sizes in the order of multiple Terabytes efficiently. The workflow was integrated into a web interface, to allow access without expertise in deep learning and batch computing. Applying deep neural networks for cytoarchitectonic mapping opens new perspectives to enable high-resolution models of brain areas, introducing CNNs to identify borders of brain areas.
AU  - Schiffer, C.*
AU  - Spitzer, H.
AU  - Kiwitz, K.*
AU  - Unger, N.*
AU  - Wagstyl, K.*
AU  - Evans, A.C.*
AU  - Harmeling, S.*
AU  - Amunts, K.*
AU  - Dickscheid, T.*
C1  - 62415
C2  - 50859
CY  - 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa
TI  - Convolutional neural networks for cytoarchitectonic brain mapping at large scale.
JO  - Neuroimage
VL  - 240
PB  - Academic Press Inc Elsevier Science
PY  - 2021
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Obesity is associated with altered responses to food stimuli in prefrontal brain networks that mediate inhibitory control of ingestive behavior. In particular, activity of the dorsolateral prefrontal cortex (dlPFC) is reduced in obese compared to normal-weight subjects and has been linked to the success of weight-loss dietary interventions. In a randomized controlled trial in overweight/obese subjects, we investigated the effect on eating behavior of volitional up-regulation of dlPFC activity via real-time functional magnetic resonance imaging (fMRI) neurofeedback training.Thirty-eight overweight or obese subjects (BMI 25-40 kg/m(2)) took part in fMRI neurofeedback training with the aim of increasing activity of the left dlPFC (dlPFC group; n = 17) or of the visual cortex (VC/control group; n = 21). Participants were blinded to group assignment. The training session took place on a single day and included three training runs of six trials of up-regulation and passive viewing. Food appraisal and snack intake were assessed at screening, after training, and in a follow-up session four weeks later.Participants of both groups succeeded in up-regulating activity of the targeted brain area. However, participants of the control group also showed increased left dlPFC activity during up-regulation. Functional connectivity between dlPFC and ventromedial PFC, an area that processes food value, was generally increased during up-regulation compared to passive viewing. At follow-up compared to baseline, both groups rated pictures of high-, but not low-calorie foods as less palatable and chose them less frequently. Actual snack intake remained unchanged but palatability and choice ratings for chocolate cookies decreased after training.We demonstrate that one session of fMRI neurofeedback training enables individuals with increased body weight to up-regulate activity of the left dlPFC. Behavioral effects were observed in both groups, which might have been due to dlPFC co-activation in the control group and, in addition, unspecific training effects. Improved dlPFC-vmPFC functional connectivity furthermore suggested enhanced food intake-related control mechanisms. Neurofeedback training might support therapeutic strategies aiming at improved self-control in obesity, although the respective contributions of area-specific mechanisms and general regulation effects are in need of further investigation.
AU  - Kohl, S.H.*
AU  - Veit, R.
AU  - Spetter, M.S.*
AU  - Günther, A.*
AU  - Rina, A.*
AU  - Lührs, M.*
AU  - Birbaumer, N.*
AU  - Preissl, H.
AU  - Hallschmid, M.
C1  - 55569
C2  - 46320
CY  - 525 B St, Ste 1900, San Diego, Ca 92101-4495 Usa
SP  - 596-609
TI  - Real-time fMRI neurofeedback training to improve eating behavior by self-regulation of the dorsolateral prefrontal cortex: A randomized controlled trial in overweight and obese subjects.
JO  - Neuroimage
VL  - 191
PB  - Academic Press Inc Elsevier Science
PY  - 2019
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Ghrelin regulates energy homeostasis in various species and enhances memory in rodent models. In humans, the role of ghrelin in cognitive processes has yet to be characterized. Here we show in a double-blind randomized crossover design that acute administration of ghrelin alters encoding-related brain activity, however does not enhance memory formation in humans. Twenty-one healthy young male participants had to memorize food- and non-food-related words presented on a background of a virtual navigational route while undergoing fMRI recordings. After acute ghrelin administration, we observed decreased post-encoding resting state fMRI connectivity between the caudate nucleus and the insula, amygdala, and orbitofrontal cortex. In addition, brain activity related to subsequent memory performance was modulated by ghrelin. On the next day, however, no differences were found in free word recall or cued location-word association recall between conditions; and ghrelin's effects on brain activity or functional connectivity were unrelated to memory performance. Further, ghrelin had no effect on a cognitive test battery comprising tests for working memory, fluid reasoning, creativity, mental speed, and attention. In conclusion, in contrast to studies with animal models, we did not find any evidence for the potential of ghrelin acting as a short-term cognitive enhancer in humans.
AU  - Kunath, N.*
AU  - Müller, N.C.*
AU  - Tonon, M.*
AU  - Konrad, B.N.*
AU  - Pawlowski, M.*
AU  - Kopczak, A.*
AU  - Elbau, I.*
AU  - Uhr, M.*
AU  - Kühn, S.*
AU  - Repantis, D.*
AU  - Ohla, K.*
AU  - Müller, T.D.
AU  - Fernández, G.*
AU  - Tschöp, M.H.
AU  - Czisch, M.*
AU  - Steiger, A.*
AU  - Dresler, M.*
C1  - 49060
C2  - 41601
CY  - San Diego
SP  - 465-473
TI  - Ghrelin modulates encoding-related brain function without enhancing memory formation in humans.
JO  - Neuroimage
VL  - 142
PB  - Academic Press Inc Elsevier Science
PY  - 2016
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups. We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation. The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction. In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success.
AU  - Emmert, K.*
AU  - Kopel, R.*
AU  - Sulzer, J.*
AU  - Brühl, A.B.*
AU  - Berman, B.D.*
AU  - Linden, D.E.*
AU  - Horovitz, S.G.*
AU  - Breimhorst, M.*
AU  - Caria, A.*
AU  - Frank, S.*
AU  - Johnston, S.*
AU  - Long, Z.*
AU  - Paret, C.*
AU  - Robineau, F.*
AU  - Veit, R.
AU  - Bartsch, A.*
AU  - Beckmann, C.F.*
AU  - van de Ville, D.*
AU  - Haller, S.*
C1  - 47264
C2  - 40621
SP  - 806-812
TI  - Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
JO  - Neuroimage
VL  - 124
PY  - 2015
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Obesity-related structural brain alterations point to a consistent reduction in gray matter with increasing body mass index (BMI) but changes in white matter have proven to be more complex and less conclusive. Hence, more recently diffusion tensor imaging (DTI) has been employed to investigate microstructural changes in white matter structure. Altogether, these studies have mostly shown a loss of white matter integrity with obesity-related factors in several brain regions. However, the variety of these obesity-related factors, including inflammation and dyslipidemia, resulted in competing influences on the DTI indices. To increase the specificity of DTI results, we explored specific brain tissue properties by combining DTI with quantitative multi-parameter mapping in lean, overweight and obese young adults. By means of multi-parameter mapping, white matter structures showed differences in MRI parameters consistent with reduced myelin, increased water and altered iron content with increasing BMI in the superior longitudinal fasciculus, anterior thalamic radiation, internal capsule and corpus callosum. BMI-related changes in DTI parameters revealed mainly alterations in mean and axial diffusivity with increasing BMI in the corticospinal tract, anterior thalamic radiation and superior longitudinal fasciculus. These alterations, including mainly fiber tracts linking limbic structures with prefrontal regions, could potentially promote accelerated aging in obese individuals leading to an increased risk for cognitive decline.
AU  - Kullmann, S.
AU  - Callaghan, M.F.*
AU  - Heni, M.
AU  - Weiskopf, N.*
AU  - Scheffler, K.*
AU  - Häring, H.-U.
AU  - Fritsche, A.
AU  - Veit, R.
AU  - Preissl, H.
C1  - 47249
C2  - 40614
SP  - 36-44
TI  - Specific white matter tissue microstructure changes associated with obesity.
JO  - Neuroimage
VL  - 125
PY  - 2015
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Brain research depends strongly on imaging for assessing function and disease in vivo. We examine herein multispectral opto-acoustic tomography (MSOT), a novel technology for high-resolution molecular imaging deep inside tissues. MSOT illuminates tissue with light pulses at multiple wavelengths and detects the acoustic waves generated by the thermoelastic expansion of the environment surrounding absorbing molecules. Using spectral unmixing analysis of the data collected, MSOT can then differentiate the spectral signatures of oxygenated and deoxygenated hemoglobin and of photo-absorbing agents and quantify their concentration. By being able to detect absorbing molecules up to centimeters deep in the tissue it represents an ideal modality for small animal brain imaging, simultaneously providing anatomical, hemodynamic, functional, and molecular information. In this work we examine the capacity of MSOT in cross-sectional brain imaging of mice. We find unprecedented optical imaging performance in cross-sectional visualization of anatomical and physiological parameters of the mouse brain. For example, the potential of MSOT to characterize ischemic brain areas was demonstrated through the use of a carbon dioxide challenge. In addition, indocyanine green (ICG) was injected intravenously, and the kinetics of uptake and clearance in the vasculature of the brain was visualized in real-time. We further found that multiparameter, multispectral imaging of the growth of U87 tumor cells injected into the brain could be visualized through the intact mouse head, for example through visualization of deoxygenated hemoglobin in the growing tumor. We also demonstrate how MSOT offers several compelling features for brain research and allows time-dependent detection and quantification of brain parameters that are not available using other imaging methods without invasive procedures.
AU  - Burton, N.C.
AU  - Patel, M.*
AU  - Morscher, S.
AU  - Driessen, W.H.P.
AU  - Claussen, J.
AU  - Bézière, N.
AU  - Jetzfellner, T.
AU  - Taruttis, A.
AU  - Razansky, D.
AU  - Bednar, B.*
AU  - Ntziachristos, V.
C1  - 28895
C2  - 33565
SP  - 522-528
TI  - Multispectral Opto-Acoustic Tomography (MSOT) of the brain and glioblastoma characterization.
JO  - Neuroimage
VL  - 65
PY  - 2013
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Alzheimer's disease (AD) disrupts selectively and progressively (increasing with severity) functional connectivity of intrinsic brain networks (IBNs), most prominent in the default mode network. Given that IBNs' functional connectivity depends on structural connectivity, we hypothesize for our study the selective and progressive changes of IBN based structural connectivity in AD. To achieve strong statistical evidence, we introduce a novel statistical method based on the edge frequency distributions of structural connectivity networks. Such non-Gaussian distributions are compared in a multiple testing scheme, combining a flexible nonparametric test statistic with permutation based strong control of the family wise error rate. We assessed 26 healthy elderly, 23 patients with AD-dementia, and 28 patients with mild cognitive impairment (MCI) by resting-state functional MRI, diffusion tensor imaging, and clinical-neuropsychological testing including annual follow-up assessment. After 3years, 50% of the patients with MCI converted to AD. Tractography of diffusion tensor data identifies structural connectivity networks between regions of IBNs, which are detected by an independent component analysis of resting state fMRI data. We find that IBNs' structural connectivity is selectively and progressively disrupted with primary changes in the default mode network. Correspondent results are found for IBNs' functional connectivity. In addition, structural connectivity across the nodes of all IBNs separated individual MCI patients converting to AD from non-converters. Conclusively, our study provides a new approach to analyze connectivity networks by their non-Gaussian edge frequency distributions and achieves strong statistical evidence by application of the family wise error rate. Data analysis provides selective and progressive disruptions of IBN's structural connectivity in AD and demonstrates the increased power of our method compared to recent studies.
AU  - Hahn, K.R.
AU  - Myers, N.*
AU  - Prigarin, S.M.*
AU  - Rodenacker, K.
AU  - Kurz, A.*
AU  - Förstl, H.*
AU  - Zimmer, C.*
AU  - Wohlschläger, A.M.*
AU  - Sorg, C.*
C1  - 24777
C2  - 31674
SP  - 96-109
TI  - Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer's disease - revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence.
JO  - Neuroimage
VL  - 81
PB  - Elsevier
PY  - 2013
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - Brain research depends strongly on imaging for assessing function and disease in vivo. We examine herein multispectral opto-acoustic tomography (MSOT), a novel technology for high-resolution molecular imaging deep inside tissues. MSOT illuminates tissue with light pulses at multiple wavelengths and detects the acoustic waves generated by the thermoelastic expansion of the environment surrounding absorbing molecules. Using spectral unmixing analysis of the data collected, MSOT can then differentiate the spectral signatures of oxygenated and deoxygenated hemoglobin and of photo-absorbing agents and quantify their concentration. By being able to detect absorbing molecules up to centimeters deep in the tissue it represents an ideal modality for small animal brain imaging, simultaneously providing anatomical, hemodynamic, functional, and molecular information. In this work we examine the capacity of MSOT in cross-sectional brain imaging of mice. We find unprecedented optical imaging performance in cross-sectional visualization of anatomical and physiological parameters of the mouse brain. For example, the potential of MSOT to characterize ischemic brain areas was demonstrated through the use of a carbon dioxide challenge. In addition, indocyanine green (ICG) was injected intravenously, and the kinetics of uptake and clearance in the vasculature of the brain was visualized in real-time. We further found that multiparameter, multispectral imaging of the growth of U87 tumor cells injected into the brain could be visualized through the intact mouse head, for example through visualization of deoxygenated hemoglobin in the growing tumor. We also demonstrate how MSOT offers several compelling features for brain research and allows time-dependent detection and quantification of brain parameters that are not available using other imaging methods without invasive procedures.
AU  - Burton, N.C.
AU  - Patel, M.*
AU  - Morscher, S.
AU  - Driessen, W.H.P.
AU  - Claussen, J.
AU  - Bézière, N.
AU  - Jetzfellner, T.
AU  - Taruttis, A.
AU  - Razansky, D.
AU  - Bednar, B.*
AU  - Ntziachristos, V.
C1  - 11229
C2  - 30564
SP  - 522-528
TI  - Multispectral Opto-Acoustic Tomography (MSOT) of the brain and glioblastoma characterization.
JO  - Neuroimage
VL  - 15
IS  - 65
PB  - Elsevier Academic Press
PY  - 2012
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - The need to study molecular and functional parameters of Alzheimer's disease progression in animal models has led to the development of disease-specific fluorescent markers. However, curved optical interfaces and a highly heterogeneous internal structure make quantitative fluorescence imaging of the murine brain a particularly challenging tomographic problem. We investigated the integration of X-ray computed tomography (CT) information into a state-of-the-art fluorescence molecular tomography (FMT) scheme and establish that the dual-modality approach is essential for high fidelity reconstructions of distributed fluorescence within the murine brain, as compared to conventional fluorescence tomography. We employ this method in vivo using a fluorescent oxazine dye to quantify amyloid-beta plaque burden in transgenic APP23 mice modeling Alzheimer's disease. Multi-modal imaging allows for accurate signal localization and correlation of in vivo findings to ex vivo studies. The results point to FMT-CT as an essential tool for in vivo study of neurodegenerative disease in animal models and potentially humans.
AU  - Hyde, D.*
AU  - de Kleine, R.*
AU  - MacLaurin, S.A.*
AU  - Miller, E.*
AU  - Brooks, D.H.*
AU  - Krucker, T.*
AU  - Ntziachristos, V.
C1  - 1329
C2  - 26054
SP  - 1304-1311
TI  - Hybrid FMT-CT imaging of amyloid-β plaques in a murine Alzheimer's disease model.
JO  - Neuroimage
VL  - 44
IS  - 4
PB  - Academic Press Inc Elsevier Science
PY  - 2009
SN  - 1053-8119
ER  - 

TY  - JOUR
AB  - The notion of fractal has been largely used to describe geometrical properties of complex objects in biology and medicine. In the present study the question is addressed whether the human cerebral cortex is self-similar in a statistical sense, which is commonly referred to as being a fractal. A new calculational method is presented, which is volumetric and based on the fast Fourier transform (FFT) of segmented three-dimensional high-resolution magnetic resonance images. The analysis covers a wide range of spatial scales from the size of the whole cortex to the ultimate pixel size. Results obtained in six subjects confirm the fractal nature of the human cerebral cortex down to a spatial scale of 3 mm. The obtained fractal dimension is D = 2.80 ± 0.05, which is in reasonable agreement with previously reported results. Deployment of FFT enables a simple interpretation of the results and yields a high performance, which is necessary to analyze the entire cortex. Thus the FFT-based analysis of segmented MR images offers a comprehensive approach to study neurodevelopmental and neurodegenerative changes in the fractal geometry of the cerebral cortex.
AU  - Kiselev, V.G.*
AU  - Hahn, K.R.
AU  - Auer, D.P.*
C1  - 22797
C2  - 31078
SP  - 1765-1774
TI  - Is the brain cortex a fractal?
JO  - Neuroimage
VL  - 20
IS  - 3
PB  - Elsevier
PY  - 2003
SN  - 1053-8119
ER  - 

TY  - JOUR
AU  - Hahn, K.R.
AU  - Prigarin, S.M.*
AU  - Pütz, B.*
C1  - 21942
C2  - 20450
SP  - 142
TI  - Regularization and Tracking for Diffusion Tensor Imaging.
JO  - Neuroimage
VL  - 13
PY  - 2001
SN  - 1053-8119
ER  - 

TY  - JOUR
AU  - Hahn, K.R.
AU  - Rodenacker, K.
AU  - Auer, D.P.*
C1  - 21943
C2  - 20451
SP  - 141
TI  - Cortex Homogenization for Intensity Segmentation - an Alternative.
JO  - Neuroimage
VL  - 13
PY  - 2001
SN  - 1053-8119
ER  - 

TY  - JOUR
AU  - Kiselev, V.G.*
AU  - Auer, D.P.*
AU  - Hahn, K.R.
C1  - 21944
C2  - 20452
SP  - 624
TI  - A fast method for calculation the fractal dimension of the brain.
JO  - Neuroimage
VL  - 11
PY  - 2000
SN  - 1053-8119
ER  -