Vesa Kiviniemi

YNIMG Journal 2026 Journal Article

Increased intracranial very low frequency pulsation power in central brain regions of high-functioning young adults with autism spectrum disorder

• Katariina Laurén
• Matti Järvelä
• Niko Huotari
• Lauri Raitamaa
• Janne Kananen
• Johanna Tuunanen
• Tommi Väyrynen
• Vesa Korhonen

Autism spectrum disorder (ASD) is an increasingly diagnosed neurodevelopmental condition characterized by persistent difficulties in social communication and restricted, repetitive patterns of behavior and sensory processing that leads to functional impairment. The diagnosis of ASD relies on behavioral and clinical assessment as there are no currently available biomarkers. Recent brain imaging studies have suggested abnormalities in the brain fluid flow in individuals with ASD. Cardiorespiratory and vasomotion-induced very low frequency (VLF ≤ 0.1 Hz) brain pulsations are now considered to facilitate the cerebrospinal- and interstitial fluid exchange in the brain, thus contributing to maintaining cerebral homeostasis and fluid clearance. In this study, we utilized ultrafast resting-state functional magnetic resonance imaging (fMRI) to capture and compare the powers of each physiological pulsation in groups of 18 young adults diagnosed with ASD and 19 neurotypical controls (NTC). We further probed the clinical significance of findings by undertaking regression analyses examining the associations of both Autism Spectrum Quotient (AQ) and Autism Diagnostic Observation Schedule (ADOS) scores with pulsation powers, and by receiver operating characteristics (ROC) analysis. Compared to the NTC group, the ASD group showed significantly higher VLF pulsation power, which was located predominantly in subcortical grey matter nuclei and the white matter, indicating increased vasomotor power in ASD. In addition, the individual VLF power enabled good accuracy (ROC area under the curve = 75%-93%) for discriminating ASD subjects from NTCs. In conclusion, present findings of increased VLF power are postulated as possible indication of altered driving force of cerebral neurofluid dynamics and could potentially serve as a useful clinical classifier.

YNIMG Journal 2025 Journal Article

Reduced harmonic complexity of brain parenchymal cardiovascular pulse waveforms in Alzheimer’s disease

• Anssi Koivula
• Vilma Perkiömäki
• Mika Aho
• Aleksi Rasila
• Valter Poltojainen
• Vesa Korhonen
• Matti Järvelä
• Niko Huotari

Alzheimer’s disease (AD) is characterized by specific neuropathologies, and is associated with arterial wall β-amyloid accumulations, which lead to radiologically detectable amplitude increases and variable propagation speed of cardiovascular impulses in brain. In this study, we developed a fast frequency domain imaging method know as relative harmonic power of magnetic resonance encephalography (MREGRHP), aiming to investigate the configuration of the cardiovascular impulses independently of the mean magnetic resonance signal intensity and physiological impulse amplitude. In the initial analyses in healthy controls, we found that a wide 0. 8 - 5 Hz bandpass produced the most physiologically realistic cardiovascular waveforms. Whereas the data recorded in cerebrospinal fluid (CSF) with flip angle (FA) of 25° yielded up to 7-fold higher cardiac signal intensity as compared to FA of 5°, within the brain tissue recordings with FA of 5° were markedly more sensitive to cardiac waveform. We detected arterial impulses originating from major arteries and extending into the surrounding brain parenchyma, with simultaneous dampening of amplitude as a function of distance from source. Finally, we compared MREGRHP results in 34 CE patients (mean age: 60. 7 ± 4. 7 years; 53 % female) against 29 controls (mean age: 56. 9 ± 7. 9 years; 66 % female). We show that the harmonic power of cardiovascular brain pulses is significantly reduced in cortical frontoparietal areas of AD patients, indicating monotonous impulse patterns colocalizing with the previously reported areas of increased impulse propagation speed. In conclusion, the MREGRHP offers a fast Fourier transform (FFT)-based method to non-invasively quantify and locate human arterial blood vessel wall pathology.

YNIMG Journal 2025 Journal Article

Ultrafast complex-valued 4D fMRI reveals sleep-induced brain respiratory pulsation changes in both magnitude and phase signals

• Saara Sofia Syväoja
• Lauri Raitamaa
• Heta Helakari
• Jussi Kantola
• Matti Järvelä
• Janne Kananen
• Ville Isokoski
• Vesa Korhonen

Physiological brain pulsations play a critical role in sleep physiology, but their underlying mechanisms remain poorly understood. To study these pulsations more deeply, we employed ultrafast magnetic resonance encephalography (MREG) to capture complex-valued 4D fMRI brain data at a critical 10 Hz sampling rate in healthy volunteers during wakefulness and sleep. We compared the phase and magnitude components of the MREG signal, as the phase component is known to be particularly sensitive to subtle flow and susceptibility changes, offering insights beyond magnitude-only analysis. This approach enabled whole-brain mapping of the amplitudes of all three physiological pulsations - very low frequency (VLF), cardiac, and respiratory - using an extended amplitude of low frequency fluctuation (ALFF) method. We identified significant increases in respiratory amplitudes during sleep compared to wakefulness in both phase and magnitude signals, while the VLF and cardiac phase amplitudes did not show significant differences. Phase respiration map showed increase especially in default mode network regions, while additional patterns were observed in the cerebellum, ventricles, cerebral aqueduct, and subarachnoid cisterns. In contrast, the magnitude maps showed increased amplitudes more widespread across the cerebrum. These findings highlight the complementary nature of phase and magnitude data in fMRI and suggest that combining these signals provides a more comprehensive understanding of brain physiological dynamics during sleep than conventional magnitude-only analyses.

YNIMG Journal 2018 Journal Article

Functional connectivity under six anesthesia protocols and the awake condition in rat brain

• Jaakko Paasonen
• Petteri Stenroos
• Raimo A. Salo
• Vesa Kiviniemi
• Olli Gröhn

Resting-state functional magnetic resonance imaging (rsfMRI) is a translational imaging method with great potential in several neurobiologic applications. Most preclinical rsfMRI studies are performed in anesthetized animals, but the confounding effects of anesthesia on the measured functional connectivity (FC) are poorly understood. Therefore, we measured FC under six commonly used anesthesia protocols and compared the findings with data obtained from awake rats. The results demonstrated that each anesthesia protocol uniquely modulated FC. Connectivity patterns obtained under propofol and urethane anesthesia were most similar to that observed in awake rats. FC patterns in the α-chloralose and isoflurane-medetomidine combination groups had moderate to good correspondence with that in the awake group. The FC patterns in the isoflurane and medetomidine groups differed most from that in the awake rats. These results can be directly exploited in rsfMRI study designs to improve the data quality, comparability, and interpretation.

YNIMG Journal 2017 Journal Article

Combined spatiotemporal ICA (stICA) for continuous and dynamic lag structure analysis of MREG data

• Ville Raatikainen
• Niko Huotari
• Vesa Korhonen
• Aleksi Rasila
• Janne Kananen
• Lauri Raitamaa
• Tuija Keinänen
• Jussi Kantola

This study investigated lag structure in the resting-state fMRI by applying a novel independent component (ICA) method to magnetic resonance encephalography (MREG) data. Briefly, the spatial ICA (sICA) was used for defining the frontal and back nodes of the default mode network (DMN), and the temporal ICA (tICA), which is enabled by the high temporal resolution of MREG (TR=100ms), was used to separate both neuronal and physiological components of these two spatial map regions. Subsequently, lag structure was investigated between the frontal (DMNvmpf) and posterior (DMNpcc) DMN nodes using both conventional method with all-time points and a sliding-window approach. A rigorous noise exclusion criterion was applied for tICs to remove physiological pulsations, motion and system artefacts. All the de-noised tICs were used to calculate the null-distributions both for expected lag variability over time and over subjects. Lag analysis was done for the three highest correlating denoised tICA pairs. Mean time lag of 0. 6s (± 0. 5 std) and mean absolute correlation of 0. 69 (± 0. 08) between the highest correlating tICA pairs of DMN nodes was observed throughout the whole analyzed period. In dynamic 2min window analysis, there was large variability over subjects as ranging between 1–10sec. Directionality varied between these highly correlating sources an average 28. 8% of the possible number of direction changes. The null models show highly consistent correlation and lag structure between DMN nodes both in continuous and dynamic analysis. The mean time lag of a null-model over time between all denoised DMN nodes was 0. 0s and, thus the probability of having either DMNpcc or DMNvmpf as a preceding component is near equal. All the lag values of highest correlating tICA pairs over subjects lie within the standard deviation range of a null-model in whole time window analysis, supporting the earlier findings that there is a consistent temporal lag structure across groups of individuals. However, in dynamic analysis, there are lag values exceeding the threshold of significance of a null-model meaning that there might be biologically meaningful variation in this measure. Taken together the variability in lag and the presence of high activity peaks during strong connectivity indicate that individual avalanches may play an important role in defining dynamic independence in resting state connectivity within networks.

YNIMG Journal 2013 Journal Article

Dynamic functional connectivity: Promise, issues, and interpretations

• R. Matthew Hutchison
• Thilo Womelsdorf
• Elena A. Allen
• Peter A. Bandettini
• Vince D. Calhoun
• Maurizio Corbetta
• Stefania Della Penna
• Jeff H. Duyn

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

YNIMG Journal 2011 Journal Article

Effects of repeatability measures on results of fMRI sICA: A study on simulated and real resting-state effects

• Jukka J. Remes
• Tuomo Starck
• Juha Nikkinen
• Esa Ollila
• Christian F. Beckmann
• Osmo Tervonen
• Vesa Kiviniemi
• Olli Silven

Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects on estimates is limited. The contribution of this study was to provide more of such information and test if the repeatability analyses are necessary. We compared FastICA-based ordinary and repeatability approaches concerning mixing vector estimates. Comparisons included original FastICA, FSL4 Melodic FastICA and original and modified ICASSO. The effects of bootstrapping and convergence threshold were evaluated. The results show that there is only moderate improvement due to repeatability measures and only in the bootstrapping case. Bootstrapping attenuated power from time courses of resting-state network related ICs at frequencies higher than 0. 1Hz and made subsets of low frequency oscillations more emphasized IC-wise. The convergence threshold did not have a significant role concerning the accuracy of estimates. The performance results suggest that repeatability measures or strict converge criteria might not be needed in sICA analyses of fMRI data. Consequently, the results in existing sICA fMRI literature are probably valid in this sense. A decreased accuracy of original bootstrapping ICASSO was observed and corrected by using centrotype mixing estimates but the results warrant for thorough evaluations of data-driven methods in general. Also, given the fMRI-specific considerations, further development of sICA methods is strongly encouraged.

YNIMG Journal 2009 Journal Article

Spontaneous Brain Activity in Motor System Predicts Simple Reaction Time Performance Several Months Later

• LIU Dongqiang
• LONG Xiangyu
• YAN Chaogan
• WANG Deyi
• ZUO Xinian
• LV Yating
• LI Sufang
• ZOU Qihong

YNIMG Journal 2005 Journal Article

BOLD signal increase preceeds EEG spike activity—a dynamic penicillin induced focal epilepsy in deep anesthesia

• Minna Mäkiranta
• Jyrki Ruohonen
• Kalervo Suominen
• Jaakko Niinimäki
• Eila Sonkajärvi
• Vesa Kiviniemi
• Tapio Seppänen
• Seppo Alahuhta

In 40–60% of cases with interictal activity in EEG, fMRI cannot locate any focus or foci with simultaneous EEG/fMRI. In experimental focal epilepsy, a priori knowledge exists of the location of the epileptogenic area. This study aimed to develop and to test an experimental focal epilepsy model, which includes dynamic induction of epileptic activity, simultaneous EEG/fMRI, and deep anesthesia. Reported results are from seven pigs (23 ± 2 kg) studied under isoflurane anesthesia (1. 2–1. 6 MAC, burst-suppression EEG) and muscle relaxant. Hypo- and hypercapnia were tested in one pig. Penicillin (6000 IU) was injected via a plastic catheter (inserted into the somatosensory cortex) during fMRI (GRE-EPI, TE = 40 ms, 300 ms/two slices, acquisition delay 1700 ms) in 1. 5 T (N = 6). Epileptic spikes between acquisition artifacts were reviewed and EEG total power calculated. Cross-correlation between voxel time series and three model functions resembling induced spike activity were tested. Activation map averages were calculated. Development of penicillin induced focal epileptic activity was associated with linear increase and saturation up to approximately 10–20%, in BOLD activation map average. Its initial linear increase reached 2. 5–10% at the appearance of the first distinguished spike in ipsilateral EEG in all six animals. Correlated voxels were located mainly in the vicinity of the penicillin injection site and midline, but few in the thalamus. In conclusion, development of focal epileptic activity can be detected as a BOLD signal change, even preceding the spike activity in scalp EEG. This experimental model contains potential for development and testing different localization methods and revealing the characteristic time sequence of epileptic activity with fMRI during deep anesthesia.

YNIMG Journal 2004 Journal Article

BOLD-contrast functional MRI signal changes related to intermittent rhythmic delta activity in EEG during voluntary hyperventilation—simultaneous EEG and fMRI study

• Minna J. Mäkiranta
• Jyrki Ruohonen
• Kalervo Suominen
• Eila Sonkajärvi
• Timo Salomäki
• Vesa Kiviniemi
• Tapio Seppänen
• Seppo Alahuhta

Differences in the blood oxygen level dependent (BOLD) signal changes were studied during voluntary hyperventilation (HV) between young healthy volunteer groups, (1) with intermittent rhythmic delta activity (IRDA) (N = 4) and (2) controls (N = 4) with only diffuse arrhythmic slowing in EEG (normal response). Subjects hyperventilated (3 min) during an 8-min functional MRI in a 1. 5-T scanner, with simultaneous recording of EEG (successful with N = 3 in both groups) and physiological parameters. IRDA power and average BOLD signal intensities (of selected brain regions) were calculated. Hypocapnia showed a tendency to be slightly lighter in the controls than in the IRDA group. IRDA power increased during the last minute of HV and ended 10–15 s after HV. The BOLD signal decreased in white and gray matter after the onset of HV and returned to the baseline within 2 min after HV. The BOLD signal in gray matter decreased approximately 30% more in subjects with IRDA than in controls, during the first 2 min of HV. This difference disappeared (in three subjects out of four) during IRDA in EEG. BOLD signal changes seem to depict changes, which precede IRDA. IRDA due to HV in healthy volunteers represent a model with a clearly defined EEG pattern and an observable BOLD signal change.

YNIMG Journal 2003 Journal Article

Independent component analysis of nondeterministic fMRI signal sources

• Vesa Kiviniemi
• Juha-Heikki Kantola
• Jukka Jauhiainen
• Aapo Hyvärinen
• Osmo Tervonen

Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA). Without deliberate neuronal activity of the brain cortex, the fMRI signal is a stochastic sum of various physiological and artifact related signal sources. The ability of spatial-domain ICA to separate spontaneous physiological signal sources was evaluated in 15 anesthetized children known to present prominent vasomotor fluctuations in the functional cortices. ICA separated multiple clustered signal sources in the primary sensory areas in all of the subjects. The spatial distribution and frequency spectra of the signal sources correspond to the known properties of 0. 03-Hz very-low-frequency vasomotor waves in fMRI data. In addition, ICA was able to separate major artery and sagittal sinus related signal sources in each subject. The characteristics of the blood vessel related signal sources were different from the parenchyma sources. ICA analysis of fMRI can be used for both assessing the statistical independence of brain signals and segmenting nondeterministic signal sources for further analysis.