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Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood. If brain signal variability is functionally relevant and serves as an important indicator of cognitive function, then it should be highly sensitive to the precise manner in which a cognitive system is engaged and/or relate strongly to differences in behavioral performance. To test this, we examined EEG activity in younger adults as they performed a cognitive skill learning task and during rest. Several measures of EEG variability and signal strength were calculated in overlapping time windows that spanned the trial interval. We performed a systematic examination of the factors that most strongly influenced the variability and strength of EEG activity. First, we examined the relative sensitivity of each measure to across-subject variation (within blocks) and across-block variation (within subjects). We found that the across-subject variation in EEG variability and signal strength was much stronger than the across-block variation. Second, we examined the sensitivity of each measure to different sources of across-block variation during skill acquisition. We found that key task-driven changes in EEG activity were best reflected in changes in the strength, rather than the variability, of EEG activity. Lastly, we examined across-subject variation in each measure and its relationship with behavior. We found that individual differences in response time measures were best reflected in individual differences in the variability, rather than the strength, of EEG activity. Importantly, we found that individual differences in EEG variability related strongly to stable indicators of subject identity rather than dynamic indicators of subject performance. We therefore suggest that EEG variability may provide a more sensitive subject-driven measure of individual differences than task-driven signal of interest., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation., Competing Interests: Declaration of competing interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Reconstructing the anatomical pathways of the brain to study the human connectome has become an important endeavour for understanding brain function and dynamics. Reconstruction of the cortico-cortical connectivity matrix in vivo often relies on noninvasive diffusion-weighted imaging (DWI) techniques but the extent to which they can accurately represent the topological characteristics of structural connectomes remains unknown. We addressed this question by constructing connectomes using DWI data collected from macaque monkeys in vivo and with data from published invasive tracer studies. We found the strength of fiber tracts was well estimated from DWI and topological properties like degree and modularity were captured by tractography-based connectomes. Rich-club/core-periphery type architecture could also be detected but the classification of hubs using betweenness centrality, participation coefficient and core-periphery identification techniques was inaccurate. Our findings indicate that certain aspects of cortical topology can be faithfully represented in noninvasively-obtained connectomes while other network analytic measures warrant cautionary interpretations., (Copyright © 2019 Elsevier Inc. All rights reserved.)

In systems neuroscience, the term "connectivity" has been defined in numerous ways, according to the particular empirical modality from which it is derived. Due to large differences in the phenomena measured by these modalities, the assumptions necessary to make inferences about axonal connections, and the limitations accompanying each, brain connectivity remains an elusive concept. Despite this, only a handful of studies have directly compared connectivity as inferred from multiple modalities, and there remains much ambiguity over what the term is actually referring to as a biological construct. Here, we perform a direct comparison based on the high-resolution and high-contrast Enhanced Nathan Klein Institute (NKI) Rockland Sample neuroimaging data set, and the CoCoMac database of tract tracing studies. We compare four types of commonly-used primate connectivity analyses: tract tracing experiments, compiled in CoCoMac; group-wise correlation of cortical thickness; tractographic networks computed from diffusion-weighted MRI (DWI); and correlational networks obtained from resting-state BOLD (fMRI). We find generally poor correspondence between all four modalities, in terms of correlated edge weights, binarized comparisons of thresholded networks, and clustering patterns. fMRI and DWI had the best agreement, followed by DWI and CoCoMac, while other comparisons showed striking divergence. Networks had the best correspondence for local ipsilateral and homotopic contralateral connections, and the worst correspondence for long-range and heterotopic contralateral connections. k-Means clustering highlighted the lowest cross-modal and cross-species consensus in lateral and medial temporal lobes, anterior cingulate, and the temporoparietal junction. Comparing the NKI results to those of the lower resolution/contrast International Consortium for Brain Imaging (ICBM) dataset, we find that the relative pattern of intermodal relationships is preserved, but the correspondence between human imaging connectomes is substantially better for NKI. These findings caution against using "connectivity" as an umbrella term for results derived from single empirical modalities, and suggest that any interpretation of these results should account for (and ideally help explain) the lack of multimodal correspondence., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The emphasis of modern neurobiological theories has recently shifted from the independent function of brain areas to their interactions in the context of whole-brain networks. As a result, neuroimaging methods and analyses have also increasingly focused on network discovery. Magnetoencephalography (MEG) is a neuroimaging modality that captures neural activity with a high degree of temporal specificity, providing detailed, time varying maps of neural activity. Partial least squares (PLS) analysis is a multivariate framework that can be used to isolate distributed spatiotemporal patterns of neural activity that differentiate groups or cognitive tasks, to relate neural activity to behavior, and to capture large-scale network interactions. Here we introduce [MEG]PLS, a MATLAB-based platform that streamlines MEG data preprocessing, source reconstruction and PLS analysis in a single unified framework. [MEG]PLS facilitates MRI preprocessing, including segmentation and coregistration, MEG preprocessing, including filtering, epoching, and artifact correction, MEG sensor analysis, in both time and frequency domains, MEG source analysis, including multiple head models and beamforming algorithms, and combines these with a suite of PLS analyses. The pipeline is open-source and modular, utilizing functions from FieldTrip (Donders, NL), AFNI (NIMH, USA), SPM8 (UCL, UK) and PLScmd (Baycrest, CAN), which are extensively supported and continually developed by their respective communities. [MEG]PLS is flexible, providing both a graphical user interface and command-line options, depending on the needs of the user. A visualization suite allows multiple types of data and analyses to be displayed and includes 4-D montage functionality. [MEG]PLS is freely available under the GNU public license (http://meg-pls.weebly.com)., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Major Depressive Disorder (MDD) is characterized by rumination. Prior research suggests that resting-state brain activation reflects rumination when depressed individuals are not task engaged. However, no study has directly tested this. Here we investigated whether resting-state epochs differ from induced ruminative states for healthy and depressed individuals. Most previous research on resting-state networks comes from seed-based analyses with the posterior cingulate cortex (PCC). By contrast, we examined resting state connectivity by using the complete multivariate connectivity profile (i.e., connections across all brain nodes) and by comparing these results to seeded analyses. We find that unconstrained resting-state intervals differ from active rumination states in strength of connectivity and that overall connectivity was higher for healthy vs. depressed individuals. Relationships between connectivity and subjective mood (i.e., behavior) were strongly observed during induced rumination epochs. Furthermore, connectivity patterns that related to subjective mood were strikingly different for MDD and healthy control (HC) groups suggesting different mood regulation mechanisms., (Copyright © 2014 Elsevier Inc. All rights reserved.)

With the increasing availability of advanced imaging technologies, we are entering a new era of neuroscience. Detailed descriptions of the complex brain network enable us to map out a structural connectome, characterize it with graph theoretical methods, and compare it to the functional networks with increasing detail. To link these two aspects and understand how dynamics and structure interact to form functional brain networks in task and in the resting state, we use theoretical models. The advantage of using theoretical models is that by recreating functional connectivity and time series explicitly from structure and pre-defined dynamics, we can extract critical mechanisms by linking structure and function in ways not directly accessible in the real brain. Recently, resting-state models with varying local dynamics have reproduced empirical functional connectivity patterns, and given support to the view that the brain works at a critical point at the edge of a bifurcation of the system. Here, we present an overview of a modeling approach of the resting brain network and give an application of a neural mass model in the study of complexity changes in aging., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Beamformers are one of the most common inverse models currently used in the estimation of source activity from magnetoencephelography (MEG) data. They rely on a minimization of total power while constraining the gain in the voxel of interest, resulting in the suppression of background noise. Nonetheless, in cases where background noise is strong compared to the source of interest, or when many sources are present, the ability of the beamformer to detect and accurately localize weak sources is reduced. In visual paradigms, two main background sources can substantially impact an accurate estimation of weaker sources. Ocular artifacts are orders of magnitude higher than neural sources making it difficult for the beamformer to effectively suppress them. Primary visual activations also result in strong signals that can impede localization of weak sources. In this paper, we systematically evaluated how neural (visual) and non-neural (eye, heart) sources affect the localization accuracy of frontal and medial temporal sources in visual tasks. These sources are of tremendous interest in learning and memory studies as well as in clinical settings (Alzheimer's/epilepsy) and are typically difficult to localize robustly in MEG. Empirical data from two tasks - active learning and control - were used to evaluate our analysis techniques. Global field power calculations showed multiple time periods where active learning was significantly different from response selection with dominant sources converging to the eyes. Extensive leakage of eye activity into frontal and visual that evoked responses into parietal cortices was also observed. Contributions from ocular activity to the reconstructed time series were indiscernible from task-based recruitment of frontal sources in the original data. Removing artifacts (eye movements, cardiac, and muscular) by means of independent component analysis (ICA) led to a significant improvement in detection and localization of frontal and medial temporal sources. We verified our results by using simulations of sources placed in frontal and medial temporal regions with various types of background noise (eye, heart, and visual). We report that the detection and localization accuracy of frontal and medial temporal sources with beamformer techniques is highly dependent on the magnitude and location of background sources and that removing artifacts can substantially improve the beamformer's performance., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objects comprise of visual and auditory signatures that arrive through distinct sensory channels. Exposure to cross-modal events sets up expectations about what a given object most likely "sounds" like, and vice versa, thereby facilitating detection and recognition. Whereas episodic and working memory functions decline with age, the extent to which multisensory integration processes change with age remains an open question. In the present study, we examined whether multisensory integration processes play a compensatory role in normal aging. Magnetoencephalography recordings of semantically-related cross-modal and unimodal auditory and visual stimuli captured the spatiotemporal dynamics of multisensory responses in young and older adults. Whereas sensory-specific regions showed increased activity in response to cross-modal compared to unimodal stimuli 100 ms after stimulus onset in both age groups, posterior parietal and medial prefrontal regions responded preferentially to cross-modal stimuli between 150 and 300 ms in the older group only. Additionally, faster detection of cross-modal stimuli correlated with increased activity in inferior parietal and medial prefrontal regions 100 ms after stimulus onset in older compared to younger adults. Age-related differences in visual dominance were also observed with older adults exhibiting significantly larger multisensory facilitation effects relative to the auditory modality. Using structural equation modeling, we showed that age-related increases in parietal and medial prefrontal source activity predicted faster detection of cross-modal stimuli. Furthermore, the relationship between performance and source activity was mediated by age-related reductions in gray matter volume in those regions. Thus, we propose that multisensory integration processes change with age such that posterior parietal and medial prefrontal activity underlies the integrated response in older adults., (Copyright © 2012 Elsevier Inc. All rights reserved.)

We assessed the hypothesis that brain signal variability is a reflection of functional network reconfiguration during memory processing. In the present experiments, we use multiscale entropy to capture the variability of human electroencephalogram (EEG) while manipulating the knowledge representation associated with faces stored in memory. Across two experiments, we observed increased variability as a function of greater knowledge representation. In Experiment 1, individuals with greater familiarity for a group of famous faces displayed more brain signal variability. In Experiment 2, brain signal variability increased with learning after multiple experimental exposures to previously unfamiliar faces. The results demonstrate that variability increases with face familiarity; cognitive processes during the perception of familiar stimuli may engage a broader network of regions, which manifests as higher complexity/variability in spatial and temporal domains. In addition, effects of repetition suppression on brain signal variability were observed, and the pattern of results is consistent with a selectivity model of neural adaptation., (Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.)

This article provides a personal perspective of the adoption of path analysis (structural equation modeling) to neuroimaging. The paper covers the motivation stemming from the need to merge functional measures with neuroanatomy and early innovations in its application. The use of path analysis as a means to test directional hypotheses about networks is presented along with the development of the complementary method, partial least squares. A method is useful when it provides insights that were previously inaccessible, and reflecting this, the paper concludes with a synopsis of the theoretical developments that arose for the routine use of methods like path analysis., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Non-invasive measuring methods such as EEG/MEG, fMRI and DTI are increasingly utilised to extract quantitative information on functional and anatomical connectivity in the human brain. These methods typically register their data in Euclidean space, so that one can refer to a particular activity pattern by specifying its spatial coordinates. Since each of these methods has limited resolution in either the time or spatial domain, incorporating additional data, such as those obtained from invasive animal studies, would be highly beneficial to link structure and function. Here we describe an approach to spatially register all cortical brain regions from the macaque structural connectivity database CoCoMac, which contains the combined tracing study results from 459 publications (http://cocomac.g-node.org). Brain regions from 9 different brain maps were directly mapped to a standard macaque cortex using the tool Caret (Van Essen and Dierker, 2007). The remaining regions in the CoCoMac database were semantically linked to these 9 maps using previously developed algebraic and machine-learning techniques (Bezgin et al., 2008; Stephan et al., 2000). We analysed neural connectivity using several graph-theoretical measures to capture global properties of the derived network, and found that Markov Centrality provides the most direct link between structure and function. With this registration approach, users can query the CoCoMac database by specifying spatial coordinates. Availability of deformation tools and homology evidence then allow one to directly attribute detailed anatomical animal data to human experimental results., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Variability or noise is an unmistakable feature of neural signals; however such fluctuations have been regarded as not carrying meaningful information or as detrimental for neural processes. Recent empirical and computational work has shown that neural systems with a greater capacity for information processing are able to explore a more varied dynamic repertoire, and the hallmark of this is increased irregularity or variability in the neural signal. How this variability in neural dynamics affects behavior remains unclear. Here, we investigated the role of variability of magnetoencephalography signals in supporting healthy cognitive functioning, measured by performance on an attention task, in healthy adults and in patients with traumatic brain injury. As an index of variability, we calculated multiscale entropy, which quantifies the temporal predictability of a time series across progressively more coarse time scales. We found lower variability in traumatic brain injury patients compared to controls, arguing against the idea that greater variability reflects dysfunctional neural processing. Furthermore, higher brain signal variability indicated improved behavioral performance for all participants. This relationship was statistically stronger for people with brain injury, demonstrating that those with higher brain signal variability were also those who had recovered the most cognitive ability. Rather than impede neural processing, cortical signal variability within an optimal range enables the exploration of diverse functional configurations, and may therefore play a vital role in healthy brain function., (Crown Copyright © 2012. Published by Elsevier Inc. All rights reserved.)

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Advances in non-invasive neuroimaging technology now provide a means of directly observing learning within the brain. Classical conditioning serves as an ideal starting point for examining the dynamic expression of learning within the human brain, since this paradigm is well characterized using multiple levels of analysis in a broad range of species. We used MEG to expand the characterization of conditioned responses (CR) recorded from the human brain with a simultaneous examination of their spatial, temporal and spectral properties. We paired an auditory conditioned stimulus (CS+) with a somatosensory unconditioned stimulus (US). We found that when the US was randomly omitted, presentations of CS+ alone, elicited greater desynchronization of beta-band activity in contralateral somatosensory cortex compared to presentations of an auditory stimulus that was never paired with the US (CS-), and compared the CS+ following a non-reinforced extinction session. This differentiation was largest between 150 and 350ms following US omission. We show that cross-modal CRs in the primary sensorimotor system are predominantly characterized by modulation of ongoing cortical oscillations., (Copyright 2010 Elsevier Inc. All rights reserved.)

Using the notion of complexity and synchrony, this study presents a data-driven pipeline of nonlinear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected in reaction to vibrostimulation of the right index finger. The dynamics of MEG source activity was reconstructed with synthetic aperture magnetometry (SAM) beam-forming technique. Considering brain as a complex system, we applied complexity-based tools to identify brain areas with dynamic patterns that remain regular across repeated stimulus presentations, and to characterize their synchronized behavior. Volumetric maps of brain activation were calculated using sample entropy as a measure of signal complexity. The complexity analysis identified activity in the primary somatosensory (SI) area contralateral to stimuli and bilaterally in the posterior parietal cortex (PPC) as regions with decreased complexity, consistently expressed in a group of subjects. Seeding an activated source with low complexity in the SI area, cross-sample entropy was used to generate synchrony maps. Cross-sample entropy analysis confirmed the synchronized dynamics of neuromagnetic activity between areas SI and PPC, robustly expressed across subjects. Our results extend the understanding of synchronization between co-activated brain regions, focusing on temporal coordination between events in terms of synchronized multidimensional signal patterns., (Copyright (c) 2010 Elsevier Inc. All rights reserved.)

This paper presents a data-driven pipeline for studying asymmetries in mutual interdependencies between distinct components of EEG signal. Due to volume conductance, estimating coherence between scalp electrodes may lead to spurious results. A group-based independent component analysis (ICA), which is conducted across all subjects and conditions simultaneously, is an alternative representation of the EEG measurements. Within this approach, the extracted components are independent in a global sense while short-lived or transient interdependencies may still be present between the components. In this paper, functional roles of the ICA components are specified through a partial least squares (PLS) analysis of task effects within the time course of the derived components. Functional integration is estimated within the information-theoretic approach using transfer entropy analysis based on asymmetries in mutual interdependencies of reconstructed phase dynamics. A secondary PLS analysis is performed to assess robust task-specific changes in transfer entropy estimates between functionally specific components.

The objective of this study was to delineate a common functional network that underlies autobiographical, episodic, and semantic memory retrieval. We conducted an event-related fMRI study in which we utilized the same pictorial stimuli, but manipulated retrieval demands to extract autobiographical, episodic, or semantic memories. To assess this common network, we first examined the functional connectivity of regions identified by a previous analysis of task-related activity that were active across all three tasks. Three of these regions (left hippocampus, left lingual gyrus, and right caudate nucleus) appeared to share a common pattern of connectivity. This was confirmed in a subsequent functional connectivity analysis using these three regions as seeds. The results of this analysis showed that there was a pattern of functional connectivity that characterized all three seeds and that was common across the three retrieval conditions. Activity in inferior frontal and middle temporal cortex bilaterally, left temporoparietal junction, and anterior and posterior cingulate gyri was positively correlated with the seeds, whereas activity in posterior occipito-temporo-parietal regions was negatively correlated. These findings support the idea that a common neural network underlies the retrieval of declarative memories regardless of memory content. This proposed network consists of increased activity in regions that represent internal processes of memory retrieval and decreased activity in regions that mediate attention to external stimuli.

Evidence from cognitive, patient and neuroimaging research indicates that "remembering to remember" intentions, i.e., prospective memory (PM) retrieval, requires both general memory systems involving the medial temporal lobes and an executive system involving rostral PFC (BA 10). However, it is not known how prospective memories are initially formed. Using fMRI, we investigated whether brain activity during encoding of future intentions and present actions differentially predicted later memory for those same intentions (PM) and actions (retrospective memory). We identified two significant patterns of neural activity: a network linked to overall memory and another linked specifically to PM. While overall memory success was predicted by temporal lobe activations that included the hippocampus, PM success was also uniquely predicted by activations in additional regions, including left rostrolateral PFC and the right parahippocampal gyrus. This finding extends the role of these structures to the formation of individual intentions. It also provides the first evidence that PM encoding, like PM retrieval, is supported by both a common episodic memory network and an executive network specifically recruited by future-oriented processing.

Memory tasks can be performed using multiple cognitive strategies, which are mediated by different brain systems. The transverse patterning (TP) task is dependent upon the integrity of the hippocampal system, however, we previously demonstrated successful TP following hippocampal damage using meaningful stimuli and relations (Moses, S.N., Ostreicher, M.L., Rosenbaum, R.S., Ryan, J.D., 2008. Successful transverse patterning in amnesia using semantic knowledge. Hippocampus 18, 121-124). Here, we used magnetoencephalgraphy (MEG) to directly observe the neural underpinnings of TP, and the changes that occur as stimuli and relations become more meaningful. In order to optimize our ability to detect signal from deep, non-dominant, brain sources we implemented the event-related synthetic aperture magnetometry minimum-variance beamformer algorithm (ER-SAM; Cheyne, D., Bakhtazad, L., Gaetz, W., 2006. Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event-related beamforming approach. Human Brain Mapping 27, 213-229) coupled with the partial least squares (PLS) multivariate statistical approach (McIntosh, A.R., Bookstein, F.L., Haxby, J.V., Grady, C.L., 1996. Spatial pattern analysis of function brain images using partial least squares. NeuroImage 3, 143-157; McIntosh, A.R., Lobaugh, N.J., 2004. Partial least squares analysis of neuroimaging data: Applications and advances. NeuroImage 23, S250-S263). We found that increased meaningfulness elicited reduced bilateral hippocampal activation, along with increased activation of left prefrontal and temporal cortical structures, including inferior frontal (IFG), as well as anterior temporal and perirhinal cortices. These activation patterns may represent a shift towards reliance upon existing semantic knowledge. This shift likely permits successful TP performance with meaningful stimuli and relations following hippocampal damage.

That the left prefrontal cortex has a critical role setting response criteria for numerous tasks has been well established, but gaps remain in our understanding of the brain mechanisms of task-setting. We aimed at (i) testing the involvement of this region in setting the criteria for a non-response and (ii) assessing functional connectivity between this and other brain regions involved in task-setting. Fourteen young participants performed a go/nogo task during functional magnetic resonance imaging. The task included two nogo visual stimuli which elicit a high (distractor) or a low (other) tendency to respond, respectively. Two task blocks were examined to assess learning the criteria. First, a multivariate Partial Least Squares (PLS) analysis identified brain regions that co-varied with task conditions, as expressed by two significant Latent Variables (LVs). One LV distinguished go and nogo stimuli. The other LV identified regions involved in the first block when the criteria not to respond to distractors were established. The left prefrontal region was prominently involved. Second, a left ventrolateral prefrontal area was selected from this LV as a seed region to perform functional connectivity using a multi-block PLS analysis. Results showed a distributed network functionally connected with the seed, including superior medial prefrontal and left superior parietal regions. These findings extend our understanding of task-setting along the following dimensions: 1) even when a task requires withholding a response, the left prefrontal cortex has a critical role in setting criteria, and 2) this region responds to the task demands within a distinctive functional network.

We used event-related potentials (ERPs) in a crossmodal stimulus-response compatibility paradigm to identify modality-independent aspects of rule processing and cued response facilitation. Participants responded to a lateralized target with the ipsilateral (compatible) or contralateral (incompatible) hand. Cue-target modality and cue-target order were manipulated. The cue preceded the target in half of the trials, and the target preceded the cue in the other half. For half of the participants, a visual cue signalled the response rule to an auditory target, while in other half, an auditory cue signalled the response rule to a visual target. Behavioural results showed a significant cue facilitation effect with response times faster for trials when the cue preceded the target, regardless of cue-target modality. The overall fastest response times were obtained in auditory cue-visual target trials. We performed groupwise independent component analysis of the cortical potentials and identified two modality-independent spatiotemporal patterns related to experimental effects. The first pattern, which resembled the early part of a contingent-negative waveform, was associated with response rule processing, regardless of cue-target presentation order and modality. The second pattern showed amplitude modulations that were dependent on stimulus modality. However, this pattern also correlated with faster response times only when the cue preceded the target and regardless of cue-target modality. Source analysis suggested that the response rule processing pattern originated from the posterior parietal, motor and cingulate regions. The pattern associated with the cue-first facilitation effect originated from cingulate and medial frontal regions. The effects carried by both patterns showed temporal overlap in the interval between the first and second stimulus presentation, but with differences in their relation to response rule processing and behavioural facilitation.

This paper focuses on two methodological developments for analysis of neuroimaging data. The first is the derivation of robust spatiotemporal activity patterns across a group of subjects using a combination of principal component analysis (PCA) and independent component analysis (ICA). In applications to ERP data, the space dimension is typically represented in terms of scalp electrodes. The signal recorded by high density electrode caps is known to be highly correlated due in part to volume conduction. Consequently, this redundancy is also reflected in spatiotemporal patterns characterizing signal differences across experimental conditions. We present an alternative spatial representation and signal compression based on PCA for dimensionality reduction and ICA conducted across all subjects and conditions simultaneously. The second advancement is the use of partial least squares (PLS) analysis to assess task-dependent changes in the expression of the independent components. In an application to empirical ERP data, we derive an efficient number of independent component maps. Comparative PLS analysis on the independent components versus original electrode data shows that task effects are not only preserved under compression, but also enhanced statistically.

To investigate the neural network of overt speech production, event-related fMRI was performed in 9 young healthy adult volunteers. A clustered image acquisition technique was chosen to minimize speech-related movement artifacts. Functional images were acquired during the production of oral movements and of speech of increasing complexity (isolated vowel as well as monosyllabic and trisyllabic utterances). This imaging technique and behavioral task enabled depiction of the articulo-phonologic network of speech production from the supplementary motor area at the cranial end to the red nucleus at the caudal end. Speaking a single vowel and performing simple oral movements involved very similar activation of the cortical and subcortical motor systems. More complex, polysyllabic utterances were associated with additional activation in the bilateral cerebellum, reflecting increased demand on speech motor control, and additional activation in the bilateral temporal cortex, reflecting the stronger involvement of phonologic processing.

Verbal fluency is a classic neuropsychological measure of language production. Phonological verbal fluency involves the generation of words beginning with a specified letter, and its functional neuroanatomy is comprised of a distributed network of regions which is modulated by cognitive load. In order to investigate the functional relationship of these regions, the effective connectivity was analyzed with covariance structural equation modeling under conditions of varying cognitive load. Significant path coefficients were evident between the anterior cingulate, left middle frontal gyrus, and precuneus. The left middle frontal gyrus showed a facilitory projection to the precuneus which had a suppressive influence on anterior cingulate activation. With increasing cognitive demand, the left middle frontal projection to the precuneus became suppressive, and the path coefficient from the precuneus to the anterior cingulate showed a marked diminution in strength. The path analysis suggests that the lead-in process for letter verbal fluency may primarily involve an orthographic visual strategy. The marked changes in path coefficients with the increased cognitive load may reflect the greater demands placed on executive function. The significant changes in path coefficient values with increased cognitive demand indicate the importance of accounting for task difficulty not only in the interpretation of brain activation maps but also for effective connectivity measurements.

Anticipatory deployment of attention may operate through networks of brain areas that modulate the representations of to-be-attended items in advance of their occurrence through top-down control. Luks and Simpson (2004) (Luks, T.L., Simpson, G.V., 2004. Preparatory deployment of attention to motion activates higher order motion-processing brain regions. NeuroImage 22, 1515-1522) found activations in both control areas and sensory areas during anticipatory deployment of attention to visual motion in the absence of stimuli. In the present follow-up analysis, we tested which network activity during anticipatory deployment of attention is functionally connected with task-related network activity during subsequent selective processing of motion stimuli. Following a cue (anticipatory phase), participants monitored a sequence of complex motion stimuli for a target motion pattern (task phase). We analyzed fMR signal using a partial least squares analysis with previously identified cue- and motion-related voxels as seed regions. The method identified two networks that covaried with the activity of seed regions during the cue and motion-stimulus-processing phases of the task. We suggest that the first network, involving ventral intraparietal sulcus, superior parietal lobule and motor areas, is related to anticipatory and sustained visuomotor attention. Operating in parallel to this visuomotor attention network, there is a second network, involving visual occipital areas, frontal areas as well as angular and supramarginal gyri, that may underlie anticipatory and sustained visual attention processes.

To perform group studies using functional imaging data, the individual brain images are usually transformed into a common coordinate space. The two most widely used spaces in the neuroscience community are the Talairach space and the Montreal Neurological Institute (MNI) space. The Talairach coordinate system has become the standard reference for reporting the brain locations in scientific publication, even when the data have been spatially transformed into different brain templates (e.g., MNI space). When expressed in terms of individual subjects, the mapping of a coordinate in MNI space to the Talairach space generates distinct coordinates for different subjects. In this paper, we describe two approaches to derive the Talairach coordinates from the MNI space, which is based on the ICBM152 template from the International Consortium of Brain Mapping. One approach is the Talairach Method of Piecewise Linear Scaling (TMPLS) as implemented in the AFNI software package; and the other is a template-matching approach using the linear transformation in SPM99. The uncertainty measurements of the mapping results are presented. This may allow researchers to better interpret results reporting in the Talairach coordinates obtained from the MNI space. This study also examines the discrepancy between the derived Talairach coordinates and those obtained from the mni2tal script, a tool commonly used by the neuroimaging community. Large discrepancies are found in the inferior regions, superior frontal and occipital regions.

Conway (Conway, M.A., 1992. A structural model of autobiographical memory. In: Conway, M.A., Spinnler, H., Wagenaar, W.A. (Eds.), Theoretical Perspectives on Autobiological Memory. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 167-194) proposed that two types of autobiographical memories (AMs) exist within a hierarchical AM system: unique, specific events and repeated, general memories. There is little research on whether retrieval of these AMs relies on different neural substrates. To investigate this issue, we used a multivariate image analysis technique, spatiotemporal partial least squares (PLS), to identify distributed patterns of activity most related to AM tasks that we have found to be associated with a medial and left-lateralized network. Using PLS, specific and general memories were more strongly associated with different parts of this retrieval network. Specific AM retrieval was associated more with activation of regions involved in imagery in episodic memory, including the left precuneus, left superior parietal lobule and right cuneus, whereas general AM retrieval was associated with activation of the right inferior temporal gyrus, right medial frontal cortex, and left thalamus. These two patterns emerged at different lags after stimulus onset, with the general AM pattern peaking between 2 and 6 s, and the specific AM pattern between 6 and 8 s. These lag differences are consistent with Conway's theory which posits that general AMs are the preferred level of entry to the AM system. A seed PLS analysis revealed that the regions functionally connected to the hippocampus during retrieval did not differentiate specific from general AM retrieval, which confirms our earlier univariate analysis indicating that some aspects of the memory retrieval network are shared by these memories.

Non-parametric statistical methods, such as permutation, are flexible tools to analyze data when the population distribution is not known. With minimal assumptions and better statistical power compared to the parametric tests, permutation tests have recently been applied to the spatially filtered magnetoencephalography (MEG) data for group analysis. To perform permutation tests on neuroimaging data, an empirical maximal null distribution has to be found, which is free from any activated voxels, to determine the threshold to classify the voxels as active at a given probability level. An iterative procedure is used to determine the distribution by computing the null distribution, which is recomputed when a possible activated voxel is found within the current distributions. Besides the high computational costs associated with this approach, there is no guarantee that all activated voxels are excluded when constructing the maximal null distribution, which may reduce the statistical power. In this study, we propose a novel way to construct the maximal null distribution from the data of the resting period. The approach is tested on the MEG data from a somatosensory experiment, and demonstrated that the approach could improve the power of the permutation test while reducing the computational cost at the same time.

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of spatiotemporal PLS (ST-PLS) to fMRI, explaining the theoretical foundation and application to an fMRI study of auditory and visual perceptual memory. Analysis of activation effects with ST-PLS was compared with conventional univariate random effects analysis, showing general consensus for both methods, but several unique observations by ST-PLS, including enhanced statistical power. The application of ST-PLS for assessment of task-dependent brain-behavior relationships is also presented. Singular features of ST-PLS include (1) no assumptions about the shape of the hemodynamic response functions (HRFs); (2) robust statistical assessment at the image level through permutation tests; (3) protection against outlier influences at the voxel level through bootstrap resampling; (4) flexible analytic configurations that allow assessment of activation difference, brain-behavior relations, and functional connectivity. These features enable ST-PLS to act as an important complement to other multivariate and univariate approaches used in neuroimaging research.

We develop a novel approach of cross-modal correspondence analysis (CMCA) to address whether brain activities observed in magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) represent a common neuronal subpopulation, and if so, which frequency band obtained by MEG best fits the common brain areas. Fourteen adults were investigated by whole-head MEG using a single equivalent current dipole (ECD) and synthetic aperture magnetometry (SAM) approaches and by fMRI at 1.5 T using linear time-invariant modeling to generate statistical maps. The same somatosensory stimulus sequences consisting of tactile impulses to the right sided: digit 1, digit 4 and lower lip were used in both neuroimaging modalities. To evaluate the reproducibility of MEG and fMRI results, one subject was measured repeatedly. Despite different MEG dipole locations and locations of maximum activation in SAM and fMRI, CMCA revealed a common subpopulation of the primary somatosensory cortex, which displays a clear homuncular organization. MEG activity in the frequency range between 30 and 60 Hz, followed by the ranges of 20-30 and 60-100 Hz, explained best the defined subrepresentation given by both MEG and fMRI. These findings have important implications for improving and understanding of the biophysics underlying both neuroimaging techniques, and for determining the best strategy to combine MEG and fMRI data to study the spatiotemporal nature of brain activity.

This paper reports the results of an across lab metanalysis of effective connectivity in major depression (MDD). Using FDG PET data and Structural Equation Modeling, a formal depression model was created to explicitly test current theories of limbic-cortical dysfunction in MDD and to characterize at the path level potential sources of baseline variability reported in this patient population. A 7-region model consisting of lateral prefrontal cortex (latF9), anterior thalamus (aTh), anterior cingulate (Cg24), subgenual cingulate (Cg25), orbital frontal cortex (OF11), hippocampus (Hc), and medial frontal cortex (mF10) was tested in scans of 119 depressed patients and 42 healthy control subjects acquired during three separate studies at two different institutions. A single model, based on previous theory and supported by anatomical connectivity literature, was stable for the three groups of depressed patients. Within the context of this model, path differences among groups as a function of treatment response characteristics were also identified. First, limbic-cortical connections (latF9-Cg25-OF11-Hc) differentiated drug treatment responders from nonresponders. Second, nonresponders showed additional abnormalities in limbic-subcortical pathways (aTh-Cg24-Cg25-OF11-Hc). Lastly, more limited limbic-cortical (Hc-latF9) and cortical-cortical (OF11-mF10) path differences differentiated responders to cognitive behavioral therapy (CBT) from responders to pharmacotherapy. We conclude that the creation of such models is a first step toward full characterization of the depression phenotype at the neural systems level, with implications for the future development of brain-based algorithms to determine optimal treatment selection for individual patients.

Partial least squares (PLS) analysis has been used to characterize distributed signals measured by neuroimaging methods like positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERP) and magnetoencephalography (MEG). In the application to PET, it has been used to extract activity patterns differentiating cognitive tasks, patterns relating distributed activity to behavior, and to describe large-scale interregional interactions or functional connections. This paper reviews the more recent extension of PLS to the analysis of spatiotemporal patterns present in fMRI, ERP, and MEG data. We present a basic mathematical description of PLS and discuss the statistical assessment using permutation testing and bootstrap resampling. These two resampling methods provide complementary information of the statistical strength of the extracted activity patterns (permutation test) and the reliability of regional contributions to the patterns (bootstrap resampling). Simulated ERP data are used to guide the basic interpretation of spatiotemporal PLS results, and examples from empirical ERP and fMRI data sets are used for further illustration. We conclude with a discussion of some caveats in the use of PLS, including nonlinearities, nonorthogonality, and interpretation difficulties. We further discuss its role as an important tool in a pluralistic analytic approach to neuroimaging.

Identification of spatiotemporal interactions within/between neuron populations is critical for detection and characterization of large-scale neuronal interactions underlying perception, cognition, and behavior. Univariate analysis has been employed successfully in many neuroimaging studies. However, univariate analysis does not explicitly test for interactions between distributed areas of activity and is not sensitive to distributed responses across the brain. Multivariate analysis can explicitly test for multiple statistical models, including the designed paradigm, and allows for spatial and temporal model detection. Here, we investigate multivariate analysis approaches that take into consideration the 4D (time and space) covariance structure of the data. Principal component analysis (PCA) and independent component analysis (ICA) are two popular multivariate approaches with distinct mathematical constraints. Common difficulties in using these two different decompositions include the following: classification of the revealed components (task-related signal versus noise), overall signal-to-noise sensitivity, and the relatively low computational efficiency (multivariate analysis requires the entire raw data set and more time for model identification analysis). Using both Monte Carlo simulations and empirical data, we derived and tested the generalized partial least squares (gPLS) framework, which can incorporate both PCA and ICA decompositions with computational efficiency. The gPLS method explicitly incorporates the experimental design to simplify the identification of characteristic spatiotemporal patterns. We performed parametric modeling studies of a blocked-design experiment under various conditions, including background noise distribution, sampling rate, and hemodynamic response delay. We used a randomized grouping approach to manipulate the degrees of freedom of PCA and ICA in gPLS to characterize both paradigm coherent and transient brain responses. Simulation data suggest that in the gPLS framework, PCA mostly outperforms ICA as measured by the receiver operating curves (ROCs) in SNR from 0.01 to 100, the hemodynamic response delays from 0 to 3 TR in fMRI, background noise models of Guassian, sub-Gaussian, and super-Gaussian distributions and the number of observations from 5, 10, to 20 in each block of a six-block experiment. Further, due to selective averaging, the gPLS method performs robustly in low signal-to-noise ratio (<1) experiments. We also tested PCA and ICA using PLS in a simulated event-related fMRI data to show their similar detection. Finally, we tested our gPLS approach on empirical fMRI motor data. Using the randomized grouping method, we are able to identify both transient responses and consistent paradigm/model coherent components in the 10-epoch block design motor fMRI experiment. Overall, studies of synthetic and empirical data suggest that PLS analysis, using PCA decomposition, provides a stable and powerful tool for exploration of fMRI/behavior data.

To investigate the difficulty that patients with Parkinson's disease (PD) have in performing fast movements, we used H(2)(15)O PET to study regional cerebral blood flow (rCBF) associated with performance of a simple predictive visuomanual tracking task at three different velocities. Tracking movements in PD patients (versus tracking with the eyes alone) were associated with a general underactivation of the areas normally activated by the task (sensorimotor cortex contralateral to the moving arm, bilateral dorsal premotor cortices, and ipsilateral cerebellum). Presupplementary motor cortex (pre-SMA) ipsilateral to the moving arm had greater than normal movement-related activations. Increasing movement velocity led to increased rCBF in multiple premotor and parietal cortical areas and basal ganglia in the patients as opposed to the few cerebral locations that are normally velocity-related. The functional correlates of PD bradykinesia are: (1) impaired recruitment of cortical and subcortical systems that normally regulate kinematic parameters of movement such as velocity; and (2) increased recruitment of multiple premotor areas including both regions specialized for visuomotor control (ventral premotor and parietal cortices) and some that are not (pre-SMA). The overactivation of cortical regions observed in patients may be functional correlates of compensatory mechanisms and/or impaired suppression as a facet of the primary pathophysiology of PD.

Electromagnetic indices of "fast" (above 12 Hz) oscillating brain activity are much more likely to be considerably attenuated by time-averaging across multiple trials than "slow" (below 12 Hz) oscillating brain activity. To the extent that both types of oscillations represent the activity of temporally and topographically separable neural populations, time averaging can cause a loss of brain activity information that is important both conceptually and for multimodal integration with hemodynamic techniques. To address this issue for recognition memory, simultaneous electroencephalography (EEG) and whole-head magnetoencephalography (MEG) recordings of explicit word recognition from 11 healthy subjects were analyzed in two different ways. First, the time course of neural oscillations ranging from theta (4.5 Hz) to gamma (42 Hz) frequencies were identified using single-trial continuous wavelet transforms. Second, traditional analyses of amplitude variations of time-averaged EEG and MEG signals, event-related potentials (ERPs), and fields (ERFs) were performed and submitted to distributed source analyses. To identify data patterns that covaried with the difference between correctly recognized studied (old) words and correctly rejected nonstudied (new) words, a multivariate statistical tool, partial least squares (PLS), was applied to both types of analyses. The results show that ERPs and ERFs are mainly displaying those neural indices of recognition memory that oscillate in the theta (4.5-7.5 Hz), alpha (8-11.5), and to some extent in the beta1 (12-19.5 Hz) frequency range. The sources of the ERPs/ERFs were in good agreement with the topography of theta/alpha/beta 1 oscillations in being confined to the anterior temporal lobe at 400 ms and being distributed across temporal, parietal, and occipital areas between 500 and 700 ms. Gamma oscillations covaried either positively or negatively with theta/alpha/beta1 oscillations. A positive covariance, for instance, was detected over left anterior temporal sensors as early as 200-350 ms and is compatible with studies in rodents showing that gamma and theta oscillations emerge together out of the interaction of the hippocampus and the entorhinal and perirhinal cortices. Fast beta oscillations (20-29.5 Hz), on the other hand, did not strongly covary with slow oscillations and were likely to arise from neural populations not adequately represented in ERPs/ERFs. In summary, by providing a more comprehensive description of electromagnetic signals, time-frequency data are of potential benefit for integrating electrophysiological and hemodynamic indices of brain activity and also for integrating human and animal electrophysiology.

Whereas some older adults show significant cognitive deficits, others perform as well as young adults. We investigated the neural basis of these different aging patterns using positron emission tomography (PET). In PET and functional MRI (fMRI) studies, prefrontal cortex (PFC) activity tends to be less asymmetric in older than in younger adults (Hemispheric Asymmetry Reduction in Old Adults or HAROLD). This change may help counteract age-related neurocognitive decline (compensation hypothesis) or it may reflect an age-related difficulty in recruiting specialized neural mechanisms (dedifferentiation hypothesis). To compare these two hypotheses, we measured PFC activity in younger adults, low-performing older adults, and high-performing older adults during recall and source memory of recently studied words. Compared to recall, source memory was associated with right PFC activations in younger adults. Low-performing older adults recruited similar right PFC regions as young adults, but high-performing older adults engaged PFC regions bilaterally. Thus, consistent with the compensation hypothesis and inconsistent with the dedifferentiation hypothesis, a hemispheric asymmetry reduction was found in high-performing but not in low-performing older adults. The results suggest that low-performing older adults recruited a similar network as young adults but used it inefficiently, whereas high-performing older adults counteracted age-related neural decline through a plastic reorganization of neurocognitive networks.

We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.

Right anterior prefrontal cortex and other brain areas are active during memory retrieval but the role of prefrontal cortex and how it interacts with these other regions to mediate memory function remain unclear. To explore these issues we used positron emission tomography to examine the effects of stimulus material and encoding task on brain activity during visual recognition, assessing both task-related changes and functional connectivity. Words and pictures of objects were encoded using perceptual and semantic strategies, resulting in better memory for semantically encoded items. There was no significant effect of prior encoding strategy on brain activity during recognition. Right anterior prefrontal cortex was equally active during recognition of both types of stimuli irrespective of initial encoding strategy. Regions whose activity was positively correlated with activity in right anterior prefrontal cortex included widespread areas of prefrontal and inferior temporal cortices bilaterally. Activity in this entire network of regions was negatively correlated with recognition accuracy of semantically encoded items. These results suggest that initial encoding task has little impact on the set of brain regions that is active during subsequent recognition. Right anterior prefrontal cortex appears to be involved in retrieval mode, reflected in its equivalent activity across conditions differing in both stimulus type and encoding task, and also in retrieval effort, shown by the negative correlation between its functional connectivity and individual differences in recognition accuracy., (Copyright 2001 Academic Press.)

An important objective of functional neuroimaging research is to identify neuroanatomical correlates of memory processes such as encoding and retrieval. In typical studies directed at this goal, however, the to-be-remembered information has been presented in a single perceptual modality. Under these conditions it is not known whether the observed brain activity reflects the studied memory process as such or only the memory process in the given modality. The positron emission tomography (PET) study reported here was designed to identify brain regions involved in encoding and retrieval processes specific to visual and haptic modalities, as well as those common to the two modalities. These latter, common regions, were assumed to be associated with "transperceptual" encoding and retrieval processes. Abstract three-dimensional objects, difficult to describe verbally, served as to-be-remembered materials. A multivariate partial least squares analysis of the PET data revealed that transperceptual encoding processes activated right medial temporal lobe, superior prefrontal cortex bilaterally, and posterior inferior temporal gyrus bilaterally. Transperceptual recognition activations were observed in two right orbitofrontal regions and in anterior cingulate. These results provide initial evidence that some processes involved in memory encoding and retrieval operate beyond perceptual processes and in that sense are transperceptual., (Copyright 2001 Academic Press.)