Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing

Real-Time Functional MRI in the Treatment of Mental Health Disorders

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.

Real-time fMRI for brain-computer interfacing

Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories: (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs.

Quality assurance of human functional magnetic resonance imaging: a literature review

Functional magnetic resonance imaging (fMRI) has been a popular approach in brain research over the past 20 years. It offers a noninvasive method to probe the brain and uses blood oxygenation level dependent (BOLD) signal changes to access brain function. However, the BOLD signal only represents a small fraction of the total MR signal. System instability and various noise have a strong impact on the BOLD signal. Additionally, fMRI applies fast imaging technique to record brain cognitive process over time, requiring high temporal stability of MR scanners. Furthermore, data acquisition, image quality, processing, and statistical analysis methods also have a great effect on the results of fMRI studies. Quality assurance (QA) programs for fMRI can test the stability of MR scanners, evaluate the quality of fMRI and help to find errors during fMRI scanning, thereby greatly enhancing the success rate of fMRI. In this review, we focus on previous studies which developed QA programs and methods in SCI/SCIE citation peer-reviewed publications over the last 20 years, including topics on existing fMRI QA programs, QA phantoms, image QA metrics, quality evaluation of existing preprocessing pipelines and fMRI statistical analysis methods. The summarized studies were classified into four categories: QA of fMRI systems, QA of fMRI data, quality evaluation of data processing pipelines and statistical methods and QA of task-related fMRI. Summary tables and figures of QA programs and metrics have been developed based on the comprehensive review of the literature.

Neu3CA-RT: A framework for real-time fMRI analysis

Real-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher. These processes are typically applied to regions of interest but, increasingly, rtfMRI techniques extract and classify whole brain functional networks and dynamics as correlates for brain states or behaviour, particularly in neuropsychiatric and neurocognitive disorders. We present Neu³CA-RT: a Matlab-based rtfMRI analysis framework aiming to advance scientific knowledge on real-time cognitive brain activity and to promote its translation into clinical practice. Design considerations are listed based on reviewing existing rtfMRI approaches. The toolbox integrates established SPM preprocessing routines, real-time GLM mapping of fMRI data to a basis set of spatial brain networks, correlation of activity with 50 behavioural profiles from the BrainMap database, and an intuitive user interface. The toolbox is demonstrated in a task-based experiment where a subject executes visual, auditory and motor tasks inside a scanner. In three out of four experiments, resulting behavioural profiles agreed with the expected brain state.

Flexible adaptive paradigms for fMRI using a novel software package 'Brain Analysis in Real-Time' (BART)

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI.

Real-time fMRI applied to pain management

Current views recognize the brain as playing a pivotal role in the arising and maintenance of pain experience. Real-time fMRI (rtfMRI) feedback is a potential tool for pain modulation that directly targets the brain with the goal of restoring regulatory function. Though still relatively new, rtfMRI is a rapidly developing technology that has evolved in the last 15 years from simple proof of concept experiments to demonstrations of learned control of single and multiple brain areas. Numerous studies indicate rtfMRI feedback assisted control over specific brain areas may have applications including mood regulation, language processing, neurorehabilitation in stroke, enhancement of perception and learning, and pain management. We discuss in detail earlier work from our lab in which rtfMRI feedback was used to train both healthy controls and chronic pain patients to modulate anterior cingulate cortex (ACC) activation for the purposes of altering pain experience. Both groups improved in their ability to control ACC activation and modulate their pain with rtfMRI feedback training. Furthermore, the degree to which participants were able to modulate their pain correlated with the degree of control over ACC activation. We additionally review current advances in rtfMRI feedback, such as real-time pattern classification, that bring the technology closer to more comprehensive control over neural function. Finally, remaining methodological questions concerning the further development of rtfMRI feedback and its implications for the future of pain research are also discussed.

Real-time fMRI: a tool for local brain regulation

Real-time fMRI permits simultaneous measurement and observation of brain activity during an ongoing task. One of the most challenging applications of real-time fMRI in neuroscientific and clinical research is the possibility of acquiring volitional control of localized brain activity using real-time fMRI-based neurofeedback protocols. Real-time fMRI allows the experimenter to noninvasively manipulate brain activity as an independent variable to observe the effects on behavior. Real-time fMRI neurofeedback studies demonstrated that learned control of the local brain activity leads to specific changes in behavior. Here, the authors describe the implementation and application of real-time fMRI with particular emphasis on the self-regulation of local brain activity and the investigation of brain-function relationships. Real-time fMRI represents a promising new approach to cognitive neuroscience that could complement traditional neuroimaging techniques by providing more causal insights into the functional role of circumscribed brain regions in behavior.

Altered functional connectivity in default mode network in absence epilepsy: a resting-state fMRI study

Dysfunctional default mode network (DMN) has been observed in various mental disorders, including epilepsy (see review Broyd et al. [2009]: Neurosci Biobehav Rev 33:279–296). Because interictal epileptic discharges may affect DMN, resting-state fMRI was used in this study to determine DMN functional connectivity in 14 healthy controls and 12 absence epilepsy patients. To avoid interictal epileptic discharge effects, testing was performed within interictal durations when there were no interictal epileptic discharges. Cross-correlation functional connectivity analysis with seed at posterior cingulate cortex, as well as region-wise calculation in DMN, revealed decreased integration within DMN in the absence epilepsy patients. Region-wise functional connectivity among the frontal, parietal, and temporal lobe was significantly decreased in the patient group. Moreover, functional connectivity between the frontal and parietal lobe revealed a significant negative correlation with epilepsy duration. These findings indicated DMN abnormalities in patients with absence epilepsy, even during resting interictal durations without interictal epileptic discharges. Abnormal functional connectivity in absence epilepsy may reflect abnormal anatomo-functional architectural integration in DMN, as a result of cognitive mental impairment and unconsciousness during absence seizure.

The left posterior superior temporal gyrus participates specifically in accessing lexical phonology

Impairments in phonological processing have been associated with damage to the region of the left posterior superior temporal gyrus (pSTG), but the extent to which this area supports phonological processing, independent of semantic processing, is less clear. We used repetition priming and neural repetition suppression during functional magnetic resonance imaging (fMRI) in an auditory pseudoword repetition task as a semantics-free model of lexical (whole-word) phonological access. Across six repetitions, we observed repetition priming in terms of decreased reaction time and repetition suppression in terms of reduced neural activity. An additional analysis aimed at sublexical phonology did not show significant effects in the areas where repetition suppression was observed. To test if these areas were relevant to real word production, we performed a conjunction analysis with data from a separate fMRI experiment which manipulated word frequency (a putative index of lexical phonological access) in picture naming. The left pSTG demonstrated significant effects independently in both experiments, suggesting that this area participates specifically in accessing lexical phonology.

Source of low-frequency fluctuations in functional MRI signal

PURPOSE

To investigate the source of native low-frequency fluctuations (LFF) in functional MRI (fMRI) signal.

MATERIALS AND METHODS

Phase analysis was performed on tissue-segmented fMRI data acquired at systematically varying sampling rates.

RESULTS

LFF in fMRI signal were both native and aliased in origin. Scanner instability did not contribute to native or aliased LFF. Aliased LFF arose from cardiorespiratory processes and head motion. Native LFF did not arise from cardiorespiratory processes, but did so, at least in part, from head motion. Motion correction reduced native LFF, but did not eliminate them. The residual native LFF in motion-corrected fMRI data showed a systematic phase difference among different tissue structures. The native LFF in fMRI signals of cerebral blood vessels and CSF were synchronous, and preceded those of gray and white matter, indicating that the vascular fluctuations lead the metabolic fluctuations.

CONCLUSION

The primary physiologic source of native LFF in fMRI signal is vasomotion.

A neural signature of phonological access: distinguishing the effects of word frequency from familiarity and length in overt picture naming

Cognitive models of word production correlate the word frequency effect (i.e., the fact that words which appear with less frequency take longer to produce) with an increased processing cost to activate the whole-word (lexical) phonological representation. We performed functional magnetic resonance imaging (fMRI) while subjects produced overt naming responses to photographs of animals and manipulable objects that had high name agreement but were of varying frequency, with the purpose of identifying neural structures participating specifically in activating whole-word phonological representations, as opposed to activating lexical semantic representations or articulatory-motor routines. Blood oxygen level-dependent responses were analyzed using a parametric approach based on the frequency with which each word produced appears in the language. Parallel analyses were performed for concept familiarity and word length, which provided indices of semantic and articulatory loads. These analyses permitted us to identify regions related to word frequency alone, and therefore, likely to be related specifically to activation of phonological word forms. We hypothesized that the increased processing cost of producing lower-frequency words would correlate with activation of the left posterior inferotemporal (IT) cortex, the left posterior superior temporal gyrus (pSTG), and the left inferior frontal gyrus (IFG). Scan-time response latencies demonstrated the expected word frequency effect. Analysis of the fMRI data revealed that activity in the pSTG was modulated by frequency but not word length or concept familiarity. In contrast, parts of IT and IFG demonstrated conjoint frequency and familiarity effects, and parts of both primary motor regions demonstrated conjoint effects of frequency and word length. The results are consistent with a model of word production in which lexical-semantic and lexical-phonological information are accessed by overlapping neural systems within posterior and anterior language-related cortices, with pSTG specifically involved in accessing lexical phonology.

Real-time functional magnetic resonance imaging: methods and applications

Functional magnetic resonance imaging (fMRI) has been limited by time-consuming data analysis and a low signal-to-noise ratio, impeding online analysis. Recent advances in acquisition techniques, computational power and algorithms increased the sensitivity and speed of fMRI significantly, making real-time analysis and display of fMRI data feasible. So far, most reports have focused on the technical aspects of real-time fMRI (rtfMRI). Here, we provide an overview of the different major areas of applications that became possible with rtfMRI: online analysis of single-subject data provides immediate quality assurance and functional localizers guiding the main fMRI experiment or surgical interventions. In teaching, rtfMRI naturally combines all essential parts of a neuroimaging experiment, such as experimental design, data acquisition and analysis, while adding a high level of interactivity. Thus, the learning of essential knowledge required to conduct functional imaging experiments is facilitated. rtfMRI allows for brain-computer interfaces (BCI) with a high spatial and temporal resolution and whole-brain coverage. Recent studies have shown that such BCI can be used to provide online feedback of the blood-oxygen-level-dependent signal and to learn the self-regulation of local brain activity. Preliminary evidence suggests that this local self-regulation can be used as a new paradigm in cognitive neuroscience to study brain plasticity and the functional relevance of brain areas, even being potentially applicable for psychophysiological treatment.

Real-time functional MRI: development and emerging applications

Real-time functional magnetic resonance imaging (fMRI) is an emerging technique for assessing the dynamic and robust changes in brain activation during an ongoing experiment. Real-time fMRI allows measurement of several processes within the brain as they occur. The extracted information can be used to monitor the quality of acquired data sets, serve as the basis for neurofeedback training, and manipulate scans for interactive paradigm designs. Although more work is needed, recent results have demonstrated a variety of potential applications for real-time fMRI for research and clinical use. We discuss these developments and focus on methods enabling real-time analysis of fMRI data sets, novel research applications arising from these approaches, and potential use of real-time fMRI in clinical settings.

Dynamic monitoring of brain activation under visual stimulation using fMRI—The advantage of real-time fMRI with sliding window GLM analysis

An fMRI technique based on real-time analysis was applied to evaluate the advantages of dynamic monitoring of the t-statistics based on a general linear model. The temporal change of the t-statistics in V1 and V4 under four conditions of visual stimuli covering different visual fields with or without coloring was estimated using an incremental analysis and a sliding window analysis (SWA). The SWA not only visualized the dynamic change of the activation in response to the task conditions and switching, but also enabled us to evaluate the temporal correlation of the t-statistics among the four visual areas. It was suggested that the activity in the V4 was bilaterally organized, and the altering color stimuli gave stronger stimulation to the V1 than did the black and white stimuli. Although the activation map at each time point represents the brain activity during several task and rest blocks, a SWA will be useful to evaluate the transition of neuronal activation in response to several sequential task conditions. An incremental analysis will be useful to monitor the ongoing activation in real-time during the scan, since it gives a higher t-value according to the accumulation of volume data. These two methods will be complementary.

Analysis of speech-related variance in rapid event-related fMRI using a time-aware acquisition system

Speech production introduces signal changes in fMRI data that can mimic or mask the task-induced BOLD response. Rapid event-related designs with variable ISIs address these concerns by minimizing the correlation of task and speech-related signal changes without sacrificing efficiency; however, the increase in residual variance due to speech still decreases statistical power and must be explicitly addressed primarily through post-processing techniques. We investigated the timing, magnitude, and location of speech-related variance in an overt picture naming fMRI study with a rapid event-related design, using a data acquisition system that time-stamped image acquisitions, speech, and a pneumatic belt signal on the same clock. Using a spectral subtraction algorithm to remove scanner gradient noise from recorded speech, we related the timing of speech, stimulus presentation, chest wall movement, and image acquisition. We explored the relationship of an extended speech event time course and respiration on signal variance by performing a series of voxelwise regression analyses. Our results demonstrate that these effects are spatially heterogeneous, but their anatomic locations converge across subjects. Affected locations included basal areas (orbitofrontal, mesial temporal, brainstem), areas adjacent to CSF spaces, and lateral frontal areas. If left unmodeled, speech-related variance can result in regional detection bias that affects some areas critically implicated in language function. The results establish the feasibility of detecting and mitigating speech-related variance in rapid event-related fMRI experiments with single word utterances. They further demonstrate the utility of precise timing information about speech and respiration for this purpose.

Adaptive pacing of visual stimulation for fMRI studies involving overt speech

We report the development of an interactive approach to single-word language production studies in fMRI. The approach, adaptive pacing, involves real-time adjustment of stimulus presentation times based on individual subject performance timing and content. At the same time, it maintains a stochastic distribution of interstimulus intervals to avoid confounding task covariates with speech-related signal variance. Adaptive pacing of overt speech production is an example of a new class of paradigms that require an observational approach to data acquisition and benefit from a "time-aware" acquisition and processing environment. The advantages of adaptive pacing in fMRI of impaired subjects are expected to be the acquisition of more informative data per unit time, less contamination of data by correlates of non-language processes such as emotion, and facilitation of experiments that combine normal and impaired subjects.

Self-regulation of local brain activity using real-time functional magnetic resonance imaging (fMRI)

Functional magnetic resonance imaging (fMRI) measures the blood oxygen level-dependent (BOLD) signal related to neuronal activity. So far, this technique has been limited by time-consuming data analysis impeding on-line analysis. In particular, no brain-computer interface (BCI) was available which provided on-line feedback to learn physiological self-regulation of the BOLD signal. Recently, studies have shown that fMRI feedback is feasible and facilitates voluntary control of brain activity. Here we review these studies to make the fMRI feedback methodology accessible to a broader scientific community such as researchers concerned with functional brain imaging and the neurobiology of learning. Methodological and conceptual limitations were substantially reduced by artefact control, sensitivity improvements, real-time algorithms, and adapted experimental designs. Physiological self-regulation of the local BOLD response is a new paradigm for cognitive neuroscience to study brain plasticity and the functional relevance of regulated brain areas by modification of behaviour. Voluntary control of abnormal activity in circumscribed brain areas may even be applied as psychophysiological treatment.

Model assessment and model building in fMRI

Model quality is rarely assessed in fMRI data analyses and less often reported. This may have contributed to several shortcomings in the current fMRI data analyses, including: (1) Model mis-specification, leading to incorrect inference about the activation-maps, SPM[t] and SPM[F]; (2) Improper model selection based on the number of activated voxels, rather than on model quality; (3) Under-utilization of systematic model building, resulting in the common but suboptimal practice of using only a single, pre-specified, usually over-simplified model; (4) Spatially homogenous modeling, neglecting the spatial heterogeneity of fMRI signal fluctuations; and (5) Lack of standards for formal model comparison, contributing to the high variability of fMRI results across studies and centers. To overcome these shortcomings, it is essential to assess and report the quality of the models used in the analysis. In this study, we applied images of the Durbin-Watson statistic (DW-map) and the coefficient of multiple determination (R(2)-map) as complementary tools to assess the validity as well as goodness of fit, i.e., quality, of models in fMRI data analysis. Higher quality models were built upon reduced models using classic model building. While inclusion of an appropriate variable in the model improved the quality of the model, inclusion of an inappropriate variable, i.e., model mis-specification, adversely affected it. Higher quality models, however, occasionally decreased the number of activated voxels, whereas lower quality or inappropriate models occasionally increased the number of activated voxels, indicating that the conventional approach to fMRI data analysis may yield sub-optimal or incorrect results. We propose that model quality maps become part of a broader package of maps for quality assessment in fMRI, facilitating validation, optimization, and standardization of fMRI result across studies and centers. Hum. Brain Mapping 20:227-238, 2003.

Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data

A brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI) is presented which allows human subjects to observe and control changes of their own blood oxygen level-dependent (BOLD) response. This BCI performs data preprocessing (including linear trend removal, 3D motion correction) and statistical analysis on-line. Local BOLD signals are continuously fed back to the subject in the magnetic resonance scanner with a delay of less than 2 s from image acquisition. The mean signal of a region of interest is plotted as a time-series superimposed on color-coded stripes which indicate the task, i.e., to increase or decrease the BOLD signal. We exemplify the presented BCI with one volunteer intending to control the signal of the rostral-ventral and dorsal part of the anterior cingulate cortex (ACC). The subject achieved significant changes of local BOLD responses as revealed by region of interest analysis and statistical parametric maps. The percent signal change increased across fMRI-feedback sessions suggesting a learning effect with training. This methodology of fMRI-feedback can assess voluntary control of circumscribed brain areas. As a further extension, behavioral effects of local self-regulation become accessible as a new field of research.

Estimation of general linear model coefficients for real-time application

An algorithm using an orthogonalization procedure to estimate the coefficients of general linear models (GLM) for functional magnetic resonance imaging (fMRI) calculations is described. The idea is to convert the basis functions or explanatory variables of a GLM into orthogonal functions using the usual Gram-Schmidt orthogonalization procedure. The coefficients associated with the orthogonal functions, henceforth referred to as auxiliary coefficients, are then easily estimated by applying the orthogonality condition. The original GLM coefficients are computed from these estimates. With this formulation, the estimates can be updated when new image data become available, making the approach applicable for real-time estimation. Since the contribution of each image data is immediately incorporated into the estimated values, storing the data in memory during the estimation process becomes unnecessary, minimizing the memory requirements of the estimation process. By employing Cholesky decomposition, the algorithm is a factor of two faster than the standard recursive least-squares approach. Results of the analysis of an fMRI study using this approach showed the algorithm's potential for real-time application.

Multiresolution data acquisition and detection in functional MRI

In an investigation of a multiresolution and multistaged approach in functional MRI, the relationship between spatial resolution and detection of functional activation is examined. The difference between functional detection and mapping is defined, and a multiresolution approach to functional detection is analyzed by constructing simple theoretical and experimental models simulating variations of in-plane resolution. Experimentally measured blood oxygenation level-dependent (BOLD) signal changes as well as BOLD contrast-to-noise ratio (CNR) with respect to different spatial resolutions are compared with results from theoretical predictions and simulation. From both an experimental and a theoretical perspective, it is shown that BOLD CNR and, thus, the concomitant detection of the functional activation are maximized when the resolution matches the size of activation.

Current awareness

In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.