Traditionally, neurobiologists have utilized microscale techniques of scientific investigation to uncover the fundamental organization a nd function of brain cells and ne uronal ensembles. In recent decades, however, macroscale brain imaging methods like magne tic resonance imaging (MRI) an d computed tomography (CT) have facilitated a wider scope of understanding neural structure and function across the lifespan. Thanks to such methods, a broader picture of the rela tionship between microscale processes— studied by neurobiologists—and macroscale observations—made by clinicians—has emerged. More recently, the vascular component o f neurodegeneration has come under renewed scrutiny partly due to increased appreciation of the relationship between neurovascula r injury, cardiovascular disease and senescence. Cerebral mi crobleeds (CMBs) are among the smallest lesions of the cerebrum which can be visualized using MRI to indicate blood-brain barrier (BBB) impa irment; as such, this class of hemorrhages are important for the evaluation and macroscale det ection of geriatric patients’ microscale pathologies associated with neur ovascular disease and/or neurod egeneration. This chapter details a streamlined protocol for MRI/CT multimodal imaging data acquisi tion, archiving and digital processing, including methods tailored for the analysis of susceptibility-w eighted imaging (SWI) and diffusion- weighted imaging (DWI) scans to reveal CMB-related alterations of the human connectome. Efficient and effective MRI/CT methods like ours, when tailored for CMB and c onnectome analysis, are essential for future progress in this important field of scientific inquiry. ii. Key Words Magnetic resonance imaging, com puted tomography, diffusion tens or imaging, susceptibility-weighted imaging, cerebral microbleed, aging, geriatrics, image processi ng, traumatic brain injury, cognitive impairment. 1. Introduction Microscale alterations effected upon the brain by neurological and metabolic disease are typically studied by cell biologists at the leve l of cells and neuronal assemblie s using microscale imaging techniques. Nevertheless, macroscale imaging methods like magnetic res onance imaging (MRI) and comp uted tomography (CT) may complement such efforts by enabling scienti sts to relate microscale pathology to macroscale findings, thereby offeri ng a broader picture of agin g and neurodegeneration. Throughout the past two decades, multisite bra in imaging studies have become i ncreasingly common, such that it is vital for imaging data to be acquire d, processed and analyzed using s treamlined workflows which minimize site-related differences in acqui sition and analysis procedures . The adequate management of imaging data acquired from older adult populations has special significance, given geriatric patients’ higher rates of overall morbidity, neurologic disease and neuropathology incide nce. For example, persons aged 65 and older are more vulnerable to tra umatic brain injury (TBI)—parti cularly mild TBI (mTBI)—and mild cognitive impairment (MCI) than any other group of adults; this can lead to substantially lower quality of life compared to younger individuals, often in tandem with card iovascular, neurovascular, neurological and metabolic diseases, inc luding dementia [1]. Improving the validity and relia bility of procedures for the an alysis of brain imaging data acquired from vulnerable populations—like the elderly—is of high priority in geriatrics and gerontology research. To achieve such improvement, research procedures and protocols mus t be described in detail, recorded thoroughly and published. Openl y available protocol description s can improve the reproducibility of neuroimaging studies, define reference workflows for the implem entation of multisite studies, and clarify methodological imprecisions to both scientists and clinicians. In recent years, the vascular component of neurodegeneration ha s received increased attention due to mounting evidence on the relations hip between neurovascular inj ury, cardiovascular disease and aging. Cerebral microbleeds (CMBs) are parenchymal lesions of the cere brum which can indicate impairment of the blood-brain barrier (BBB). They may be small as ~1 mm3 on certain types of MRI [1], and are diagnostically important in geriatric patients because they rep resent macroscale manifestations of microscale pathological processes associated with neurovascular disease and neurodegeneration. This chapter describes a set of sta ndardized, streamlined methods fo r the acquisition, processing and comprehensive analysis of CMB- positive MRI/CT scans of the agin g brain, including the specification of suitable inclusion and exclusion criteria for older participant s, MRI and CT scanner specifications, MRI imaging sequence parameters, data archiving strategies and proc essing methods for both CT and MRI, including T1- and T2-weighted anatomic MRI, diffusion-weighted imaging (DWI) and su sceptibility- weighted imaging (SWI, which can assist in identifying CMBs) [1 ]. 2. Methods The first part of this section outlines procedures to recruit v olunteers for imaging studies like those previously undertaken by our laboratory. This is followed by a description of our data acquisition protocol, and then by a detailed narrative of data archiving an d processing steps to prepare data for subsequent CMB analysis and connectomics. 2.1 Inclusion and Exclusion Criteria The following are inclusion and exclusion criteria for older he althy control (HC) participants, for individuals with MCI and for TBI survivors. Inclusion criteria for all groups 1. Be at least 40 years of age 2. Speak English fluently 3. Be competent to provide informed consent Exclusion criteria for all groups 1. A diagnosis of comorbid neurol ogical or psychiatric disease 2. Being currently nursing or pregnant, or intending to become pre gnant 3. Not speaking English fluently 4. Suffering from AIDS 5. Suffering from claustrophobia 6. Having any metal objects in the body which are affected by stro ng magnetic fields Additional, group-specific inclusion criteria 1. For TBI participants, the volunt eer must have experienced one o r more TBIs 2. For MCI participants, the volunt eer must have reported memory c oncerns 2.2 Data Acquisition and Archiving MRI acquisition. MRIs are acquired using a whole-body, MAGNETOM Prisma FIT 3T scanner (Siemens Corporation, Erlagen, Germany) w ith a 60 cm bore diameter. All head scans are acquired using a 20- channel head-neck matrix coil. The following are the technical specifications of the PrismaFIT scanner:  magnetic homogeneity of 0.103 parts per million (ppm) within th e 40 cm diameter spherical volume of the bore  64 parallel radiofrequency (RF) receiver channels and up to 204 coil elements  dual-channel parallel transmit and slice-accelerated “multi-ban d” acquisition  A total imaging matrix (TIM) of 204 × 64  A 2x Tesla C2075 graphics processing unit (GPU)  A XR 80/200 gradient system with a maximum amplitude of 80 mT/m per axis and a maximum slew rate of 200 T/m/s per axis  Active shimming using 3 linear and 5 second-order channels  A field of view (FOV) of at most 500 mm and at least 5 mm  A maximum matrix size of 1024 × 1024  A maximum in-plane resolution of 7 µm  Integrated Tx/Rx solid-state DirectRF technology  Syngo MR VD13D software A total of 10 sequences are incl uded in this prot ocol for acqui ring MRI scans. The sequence names and their parameters are listed in Table 1 . CT Acquisition. CT scans are acquired using a 16-slice General Electric (GE) Br ightSpeedTM scanner. Images are acquired using th e following parameters:  width: 512 pixels  height: 512 pixels  mode: helical  slice thickness: 0.6250 mm  slice spacing: 0.6250 mm  kilovoltage peak (kVp): 120 kV  data collection diameter: 250 mm  reconstruction diameter: 223 mm  gantry detector tilt: 0 degrees  rotation direction: clockwise  exposure time: 1397 ms  X-ray tube current: 140 mA  filter type: head filter  generator power: 16800 kW  focal spot: 0.7 mm  convolution kernel type: soft  revolution time: 1 s  single collimation width: 0.625 mm  total collimation width: 10 mm  table speed: 9.3750 mm/s  table feed per rotation: 9.375 mm  spiral pitch factor: 0.9375  pixel spacing: 0.435547 mm MRI/CT Archiving. After acquisition, data are stor ed in DICOM (digital imaging an d communications in medicine) format. They are th en de-identified and de-linked to preserve privacy and confidentiality. This is achieved by removing identifying information from the D ICOM file headers. Next, data are transferred to a secure online database managed by the scanning facility ( see Note 1). Following this transfer, data are transferred to a depository maintained by th e laboratory, where they can be accessed and processed at ease ( see Note 2). 2.3 Neuroimage Data Processing All data processing steps are b ased upon protocols used in past studies conducted in our laboratory [2-7]. This section describes preprocessing steps for both MRI and CT scans. A set of universal MRI processing steps are applied to all MRI volumes, whereas DWI and SWI data undergo additional processing steps which are specific only to these two types of data. All steps f or universal MRI processing and DTI are undertaken using the fMRI Standard Library (FSL) image processi ng toolbox (fsl.fmrib.ox.ac.uk/fsl). MRI Preprocessing Steps. The following set of preprocessing steps are applied to all MRI volumes, including those acquired using DWI and SWI sequences. 1. DICOM files are converted into NIFTI format using dcm2niix (https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage ) 2. Motion correction is performed 3. Voxels beyond the area of the b rain are excluded (i.e. imaging volumes are skull-stripped) 4. Image intensities within the brain mask are identified 5. The intensities of voxels within t he brain are normalized acros s subjects by dividing their intensities by their median intensity ( see Note 3 ). 6. Bias field correction is applied to each volume using a fourth- order polynomial [8]. DWI Processing Steps. The following steps are applied to DWI data in addition to thos e previously described. 1. B0 volumes are identified as those volumes acquired while no diff usion gradient is being applied 2. Skull removal is implemented a nd the brain mask is computed 3. Susceptibility artefact correction is implemented 4. Motion correction is applied 5. Eddy-current artefact correction is performed 6. A suitable rotation of the B mat rix is applied to alleviate ori entation artefacts ( see Note 4 ). 7. B0 images are co-registered to their corresponding T1-weighted volumes using a rigid, affine (six-parameter) registration 8. Diffusion tensors are fitted to DWI images 9. FA calculations are performed 10. Deterministic tractography via fi xed step-length streamline pro pagation is used to reconstruct white matter (WM) streamlines us ing the following parameters:  seed spacing: 0.5 mm  linear measure start threshold: 0.3  stopping value: 0.17  minimum streamline length: 20 mm  maximum streamline length: 110 mm  stopping criterion: FA value  stopping criterion value: 0.17  stopping track curvature: 0.96 mm-1  integration step length: 0.5 mm 11. Streamlines are visualized in TrackVis (trackvis.org) and 3D Sl icer (www.slicer.org) If longitudinal MRI/DWI data are available, one can estimate an d reduce the extent to which diffusion tractography measures can be influenced by confounding factors, such as subject motion, scanner noise, MRI inhomogeneity differences and other computational errors an d inaccuracies. Such estimation is completed through the following technique: 1. At each WM voxel, the uncertainty ellipsoid is defined as a vol ume around a voxel where any DTI-derived WM measures require detailed analysis, since they m ay be influenced by confounding factors to a greater extent ( see Note 5). 2. If quantification of WM changes over time is required, the foll owing steps are taken:  streamlines are assumed to be pi ecewise-differentiable 3D space curves with an invariant curvature and torsion  any two curves are determined to have equal curvature and torsi on if they lie within the same transformation of each other and if there is a curve index correspondence between parametrizations  Within each subject, curve index correspondence is established [9] by generating arc length parametrizations for each curve, time point and perilesi onal streamline bundle  Point-to-point co-registration is performed by implementing a l ocal transformation using Schönemann’s solution to the ort hogonal Procrustes problem [10] .  The global residue of squared in ter-curve distances within the local transformations is minimized to approximate a global transformation which matches source and target curves  FA values and pointwise descriptiv e statistics of each measure are calculated for the arc- length coordinates of each streamline [9] and [11]  The range of arc-length coordinat es shared between most streaml ines within each bundle of interest is identified  To compare mean FA differences over time within perilesional st reamline bundles and then evaluate whether or not they may be due to artefacts, two empirical reference mean FA distributions are establish ed (one for younger subjects and one for older subjects)  The mean and variance of the ref erence distributions are calcul ated over the streamlines  Distribution parameter values are pooled within each age group  The null hypothesis of no differe nce in FA across time points i s tested for each perilesional streamline bundle of interest. 3. WM streamline prototyping is implemented as follows:  An anatomical clustering atlas is used to annotate and categori ze WM streamline clusters (see Note 6 ) as deep WM bundles (such as major association and projection bundles, commissural bundles, cerebellar a nd brainstem connection bundle s), short bundles and medium-range superficial bundles, which are organized by the br ain lobes that they connect  Connections identified as potential false positives are annotat ed for exclusion  Following WM parcellation, WM prototyping is implementing by id entifying the most representative WM streamline in each cluster, i.e. the streamli ne whose trajectory is most similar to the average traject ory of the streamline in the bund le (see Note 7 ). SWI Processing Steps. The following steps are implem ented to process SWI volumes and identify CMBs: 1. SWI volumes are skull-stripped and co-registered rigidly to the ir respective T1-weighted volumes 2. CMB identification is accomplishe d using the following algorith ms  The image intensity gradient of t he skull-stripped SWI volume i s computed at each brain voxel using MATLAB  Binary erosion of the gradient ma gnitude volume is performed us ing a spherical morphological structuring elemen t (radius = 11 pixels) to remov e areas exhibiting sharp gradient changes at the edges of the brain  A gradient magnitude histogram i s calculated over all brain vox els.  The eroded volume is thresholded to exclude values belonging to the tails of its intensity distribution  Within the thresholde d volume, the volume of each connected com ponent consisting of contiguous non-zero voxels is computed  The largest component–associated with voxels on the borders bet ween cerebrospinal fluid (CSF) and WM—is removed  Components associated with voxels within the sulcal banks of th e neocortex and on the boundaries between brain and blood vessels are eliminated by re moving voxels which were identified as isointense (i .e. within µ ± 2σ of the image intensity distribution of the SWI image)  Remaining non-zero voxel component s are assigned CMB labels. The sensitivity and precision of the CMB identification method can be evaluated against a manual CMB labelling method conducted by a ne uroanatomy and neuroradiology expert. Specifically, to calculate the sensitivity and precision of the a pproach, a manual detection a lgorithm is used, as follows. 1. Potential CMBs are identified in images using a voxel classifie r based on Microbleed Anatomic Rating Scale (MARS) guidelines [12]. 2. Identified CMBs are classified as either certain (represented b y small, clearly delineated circular shapes) or doubtful (re presented as components which a re not well-delineated and which do not conform to strictly circular shapes) 3. Hypointensities on the boundary of the brain are disregarded, a s CMBs are not associated with the meninges 4. An object-based classifier which assesses CMB shape is applied to eliminate false positives CMB assignments 5. CMB sphericity violations are determined by calculating CMB obj ect curvature and identifying values which exceed t he curvature of a sphere by a substantial amount [13]. 6. If CMB etiology must be classified as either traumatic or non-t raumatic (e.g. related to cerebral amyloid angiopathy or h ypertensive vasculopathy), a CM B is assumed to be TBI- related if (A) at the acute sta ge of TBI, the CMB is partially or fully surrounded by one or more focal FLAIR hyperintensities, and (B) at the chronic stage of TBI, the FLAIR hyperintensities are no longer detectable. CT Processing Steps. CT segmentation is performed u sing an approach whose results ar e presented elsewhere [14-19]. The steps of th e algorithm are as follows. 1. The parameters of Gaussian inten sity distributions are computed for three tissue classes [WM, grey matter (GM) and cerebrospinal fluid (CSF)] 2. Using the following steps, pr obabilities are assigned to voxels to reflect their likelihood of belonging to a specific tissue cl ass in the Gaussian mixture mo del:  An objective function is derived from several Gaussian random v ariable models and its value is minimized using a nonlinear optimization procedure of the user’s choice  To complete the classification process, a priori tissue probability maps available in a modified MINI 152 probabilistic atlas [20] are ide ntified for each tissue class  To accommodate the intensity pro file of CT scans—where CSF is h ypointense—the atlas is modified to assign low intensities to CSF voxels in the atla s  The objective function is used to weigh the probability maps us ing Bayesian inference principles  The probability maps are deform ed to match each subject’s brain CT volumes to be segmented  The volumes are segmented by assigning probabilities to each vo xels, which specify the likelihood that the voxel belongs to a certain tissue class  Posterior probabilities are com puted according to each subject’ s voxel intensity values  The interface between the resulti ng GM and WM volumes is smooth ed using a nonlinear Wiener filter, subject to the topological constraints of the br ain’s intensity profile.  Topology-constrained probabilistic classification refinement is performed [16] in three steps: a. The plane which crosses the WM /GM boundary and is intersected b y voxels with minimal intensity variance is identified b. Voxels in the identified plane are assigned according to whethe r most of them have an unclear intensity classifica tion, or whether they are close to voxels with varying class memberships c. A voxel’s class assignment is cha nged to the more appropriate o ne if this reduces the in-plane density variance Figure 1 shows the output produ ced by the preprocessing of DWI and SWI scans acquired from a sample geriatric mTBI patient. 3. Notes 1. Interaction with the secure dat abase to which the MRI scans wer e uploaded is facilitated by Flywheel software (flywheel.io). 2. MRI data transfer is made to a H IPAA-compliant Amazon Web Servi ces (AWS) online cloud environment 3. Intensity normalization is imple mented to account for the poten tially confounding effect of inter- subject intensity brain differences. 4. The additional rotation of the B matrix in DWI image processing is necessary because resulting orientational information may be affected by the increased head motion which is often associated with DWI, given this modality’s relatively long acquisition tim es [21]. 5. In the estimation of confoundi ng factors, the use of the ellips oid—rather than that of a sphere—is advantageous because it facilitat es the evaluation of the local water-diffusion WM anisotropy 6. The anatomically-curated stream line clustering atlas used is de scribed in reference [6]. This atlas is suitable because it allows the w hole-brain WM to be parcelled i nto 800 streamline clusters. 7. The approach to WM prototyping was based upon methods described elsewhere [3,9,22,6,7]. Streamline prototyping is important in diffusion MRI tractograp hy, as the latter technique may generate false streamlines which are not representative of real WM connections. Establishing a streamline prototype for an entir e cluster allows for a more ac curate detection of underlying WM structure and of its trajectory. Acknowledgment This work was supported by NIH g rant R01 NS 100973, by DoD cont ract W81-XWH-1810413 and by the Undergraduate Research Associate Program (URAP) at the Univ ersity of Southern California. The authors wish to thank Nikhil N. Chaudhari, Nahian F. Chowdhury, and Kenneth A. Rostowsky for their comments and suggestions, as we ll as Drs. Lauren J. O’Donnell a nd Fan Zhang (Harvard Medical School) for their advice on DWI preprocessing. AI would like to thank t he Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technolo gy, where part of this research was carried out. 4.